Reviewerly/exHarmony huggingface dataset:
Word-Level Contextual Sentiment Analysis with Interpretability

Word-level contextual sentiment analysis (WCSA) is an important task for mining reviews or opinions. When analyzing this type of sentiment in the industry, both the interpretability and practicality are often required. However, such a WCSA method has not been established. This study aims to develop a WCSA method with interpretability and practicality. To achieve this aim, we propose a novel neural network architecture called Sentiment Interpretable Neural Network (SINN). To realize this SINN practically, we propose a novel learning strategy called Lexical Initialization Learning (LEXIL). SINN is interpretable because it can extract word-level contextual sentiment through extracting word-level original sentiment and its local and global word-level contexts. Moreover, LEXIL can develop the SINN without any specific knowledge for context; therefore, this strategy is practical. Using real textual datasets, we experimentally demonstrate that the proposed LEXIL is effective for improving the interpretability of SINN and that the SINN features both the high WCSA ability and high interpretability.
Improved Mutual Information Estimation

We propose to estimate the KL divergence using a relaxed likelihood ratio estimation in a Reproducing Kernel Hilbert space. We show that the dual of our ratio estimator for KL in the particular case of Mutual Information estimation corresponds to a lower bound on the MI that is related to the so called Donsker Varadhan lower bound. In this dual form, MI is estimated via learning a witness function discriminating between the joint density and the product of marginal, as well as an auxiliary scalar variable that enforces a normalization constraint on the likelihood ratio. By extending the function space to neural networks, we propose an efficient neural MI estimator, and validate its performance on synthetic examples, showing advantage over the existing baselines. We demonstrate its strength in large-scale self-supervised representation learning through MI maximization.
TEACHERS’ DIGITAL COMPETENCE DEVELOPMENT AS AN IMPORTANT FACTOR FOR THE CREATION AND SUPPORT OF THE STEAM-BASED EDUCATIONAL ENVIRONMENT

The article is focused on studying teachers’ digital competence as a significant factor for the creation and support of the STEAM-based educational environment. The purpose of the article is to analyze teachers’ experience in using information and communication technology (ICT) for the STEAM-education support in secondary schools and highlight the main problems which may arise during this process. This research explores the following questions: What is the role of the teachers’ digital competence in creating and supporting the STEAM-based educational environment? What are the ways to solve the problem of insufficient teachers’ digital competence for creating and supporting the STEAM-based educational environment?The teachers’ digital competence development is viewed as the most important for creating and supporting the STEAM-based educational environment in schools.The main ways to solve the problem of teachers’ insufficient digital compe-tence for creating and supporting the STEAM-based educational environment are as follows: creating ICTs courses for teachers to support STEAM-education and cooperative learning and teaching activities in schools; organizing centers, courses, conferences, trainings, webinars, etc. on STEAM-education for teachers who will coordinate international and national projects; monitoring students’ skills in STEM-education; engaging teachers, students of general education schools and institutions of informal learning, scientists and researchers, etc. to develop the virtual STEAM-based educational environment.Creating the STEAM-based educational environment in general education schools and other educational institutions is a research perspective, which will contribute to developing the teachers’ digital competence in using ICT in STEAM-education.
A Quantum Protocol for Secure Manhattan Distance Computation

Secure Manhattan distance computation allows two parties to privately compute the Manhattan distance of their points, which is important and has broad applications.In this paper, we present a protocol for secure Manhattan distance computation based on phase shift operation.Because of quantum mechanics' physical principle, our protocol can also resist outside attacks and don't leak any private information of participants.
DPAEG: A Dependency Parse-Based Adversarial Examples Generation Method for Intelligent Q&amp;A Robots

Recently, the natural language processing- (NLP-) based intelligent question and answer (Q&amp;A) robots have been used ubiquitously. However, the robustness and security of current Q&amp;A robots are still unsatisfactory, e.g., a slight typo in the user’s question may cause the Q&amp;A robot unable to return the correct answer. In this paper, we propose a fast and automatic test dataset generation method for the robustness and security evaluation of current Q&amp;A robots, which can work in black-box scenarios and thus can be applied to a variety of different Q&amp;A robots. Specifically, we propose a d ependency p arse-based a dversarial e xamples g eneration (DPAEG) method for Q&amp;A robots. DPAEG first uses the proposed dependency parse-based keywords extraction algorithm to extract keywords from a question. Then, the proposed algorithm generates adversarial words according to the extracted keywords, which include typos and words that are spelled similarly to the keywords. Finally, these adversarial words are used to generate a large number of adversarial questions. The generated adversarial questions which are similar to the original questions do not affect human’s understanding, but the Q&amp;A robots cannot answer these adversarial questions correctly. Moreover, the proposed method works in a black-box scenario, which means it does not need the knowledge of the target Q&amp;A robots. Experiment results show that the generated adversarial examples have a high success rate on two state-of-the-art Q&amp;A robots, DrQA and Google Assistant. In addition, the generated adversarial examples not only affect the correct answer (top-1) returned by DrQA but also affect the top- k candidate answers returned by DrQA. The adversarial examples make the top- k candidate answers contain fewer correct answers and make the correct answers rank lower in the top- k candidate answers. The human evaluation results show that participants with different genders, ages, and mother tongues can understand the meaning of most of the generated adversarial examples, which means that the generated adversarial examples do not affect human’s understanding.
Cognitive Styles in Solving Educational Tasks

The article considers the influence of cognitive styles and individual features of perception and coding information on teaching geometry. Cognitive styles can determine success in learning, as they are manifested in the individual characteristics of the cognitive processes of students, their ways of perception, thinking, and action. Special attention is paid to the problems of connection of cognitive styles with interhemispheric brain asymmetry, level of intelligence, and motivation of a personality. The article underlines a very important for the learning process individual characteristic of a student. It is ‘differentiation of the field’, which means the influence of differences of the external environment on human decision-making. Differentiation of the field is described in terms of field dependence – field independence. Field dependence as a cognitive style means dominance of the whole, a lack of differentiation of parts in an image of perception, a disability to overcome the context, to select separate stimuli from the background. Field independence is an ability to resist the influence of conflicting background signs in the perception of visual forms and ties, an ability to perceive the whole, to allocate parts from complex figures, to choose stimuli from a context. In the conducted experimental research there were offered examples of tasks that allow to form cognitive ‘flexibility’ and overcome difficulties of students in analyzing geometric problems text. There were highlighted types of such tasks that were characterized by an educational effect. The conclusions from the experiment show that taking into account the parameters of cognitive styles with regular use of the system of developed tasks has a positive impact on the achievement of subject results in teaching students. The analysis of scientific and educational literature was carried out in order to make the understanding of the categories in this insufficiently researched area available to a wide range of school teachers.
Managerial Potential of Educational Organizations under Conditions of Network Interaction

Development of new forms of labor self-organization is embodied in phenomena of distributed management and direct influence of personnel on realization and development of the organization’s strategy. The article is devoted to studying of relations underlying the development of managerial potential of an educational organization under conditions of network interaction. The work involves activity, network, system-synergetic approaches and methods of comparative theoretical and methodological analysis, theoretical reconstruction of bases of development strategies of a modern educational organization’s managerial potential under conditions of network interaction. Differences of the network and non-network managerial paradigms in education are described in detail. It was established that the managerial potential of educational organizations is actively realized in course of development of collectives’ involvement into solving and realization of strategic and tactic tasks in conditions of activity of the scientific-educational network. It is demonstrated that development of the managerial potential of interaction participants is provided via creation of additional managerial roles and solving of super-functional managerial tasks, which allows to change the nature of the managerial labor. It is demonstrated that managerial potential of teachers’ collectives can successfully developed in integrative management technologies based on a new methodology focused on development of involvement of network interaction into solving of organizational issues, solving of strategic and tactic tasks under conditions of systemic activity of the scientific-educational network.
Embedding Adaptation Levels within Intelligent Tutoring Systems for Developing Programming Skills and Improving Learning Efficiency

Intelligent Tutoring Systems (ITSs) represent the virtual learning environment that provides learning needs, adapts to the characteristics of learners according to their cognitive and behavioral aspects, to reach desired learning outcomes.The purpose of this study is to investigate the impact of embedding some adaptation levels within intelligent tutoring systems on developing Object-Oriented Programming skills (OOP), as well as on learning efficiency for students of the computer science department, Faculty of Science and Arts at Qassem University.In this context, the author developed an Intelligent Tutoring System (ITS) that provides multiple levels of adaptation (Learner level, links level) to support automatic adaptation to each of the students' characteristics and investigate the effectiveness of the system on dependent variables.The random sample consisted of (n=44) students.Those students were divided into two similar groups, Experimental an (ITS), and Control (face-to-face, traditional).The findings revealed that there was a noticeable improvement in the students' performance for the experimental group than the control group how used faceto-face method for the programming skills and learning efficiency.
Adaptive Interpolation Scheme for Image Magnification Based on Local Fractal Analysis

In this paper, we constructed a new rational fractal interpolation model with scale factors and shape parameters. The model obtains different expressions by changing the scale factor, according with the diversity of image features. On the basis of the constructed model, this paper presents a novel adaptive rational fractal magnification (ARFM) algorithm based on local fractal feature analysis. Firstly, the image is divided into different regions according to an adaptive threshold determined by a calculated fractal dimension, and the scaling factor is calculated based on the relationship between global fractal dimension (GFD) and local fractal dimension (LFD). Secondly, for different regions, different interpolation forms are selected according to regional characteristics. Thirdly, parameter optimization and sub-block selection are studied to further enhance the quality of the magnified image. The experimental results illustrate that the performance of the proposed ARFM algorithm is very competitive compared with the latest magnification algorithms.
A Comparison of Selected Bibliographic Database Search Retrieval for Agricultural Information

Search result retrieval was compared across eight research literature databases (AGRICOLA, AGRIS, BIOSIS, CAB Direct, FSTA, Google Scholar, Scopus, and Web of Science) for three topics from different agricultural disciplines to compare retrieval results based on searcher experience. Precision, recall, and uniqueness were analyzed by rating search results (~2400 citations) for relevancy. A generalized linear model statistical analysis determined that AGRICOLA ranked highest for precision and was statistically more likely to produce a relevant result than four other databases. CAB and Web of Science ranked highest for recall and both overlapped with AGRICOLA for statistical likelihood of producing a relevant result. Google Scholar retrieved the most unique content, but almost half of that content was not judged relevant. AGRICOLA, BIOSIS and CAB retrieved the most unique and relevant content. This study will help researchers and librarians working in the agricultural disciplines to select the bibliographic databases that will provide the most relevant search results and are most likely to meet their research need. It may also serve as a template for future bibliographic research in other disciplines.
TASP: A Time-Phased Model for Sepsis Prediction

Background: As a lift-threatening condition, sepsis is one of the major public health issues around the world. Early prediction can improve the sepsis outcomes by prompt treatmentMethod: As part of the Physionet/Computing in Cardiology Challenge 2019, our team (FlyingBubble) proposed a Time-phAsed model for Sepsis Prediction (TASP). Realizing the fact that the incidence of sepsis is time-dependent, our model is a fusion of different frameworks along the time dimension. In the beginning stage of ICU stay, a gradient boosting tree model is used to figure out the patients with relatively high risk of sepsis. Following that, another tree model with more features is adopted to identify the risk in middle stage. If a patient stayed in ICU more than 50 hours, a deep learning framework will be used to capture the long-term relations for sepsis risk prediction in late stage. We construct proper features for each sub-models with different missing value imputation strategiesResult: The proposed model obtains a score of 0.415 by means of 10-fold cross-validation on the training set. Two simplified versions of the model respectively get scores 0.420 and 0.419 on official online test set A. And the higher one is ranked in 4th with score 0.337 on full test set.
TsmartGP: A Tool for Finding Memory Defects with Pointer Analysis

Precise pointer analysis is desired since it is a core technique to find memory defects. There are several dimensions of pointer analysis precision, flow sensitivity, context sensitivity, field sensitivity and path sensitivity. For static analysis tools utilizing pointer analysis, considering all dimensions is difficult because the trade-off between precision and efficiency should be balanced. This paper presents TsmartGP, a static analysis tool for finding memory defects in C programs with a precise and efficient pointer analysis. The pointer analysis algorithm is flow, context, field, and quasi path sensitive. Control flow automatons are the key structures for our analysis to be flow sensitive. Function summaries are applied to get context information and elements of aggregate structures are handled to improve precision. Path conditions are used to filter unreachable paths. For efficiency, a multi-entry mechanism is proposed. Utilizing the pointer analysis algorithm, we implement a checker in TsmartGP to find uninitialized pointer errors in 13 real-world applications. Cppcheck and Clang Static Analyzer are chosen for comparison. The experimental results show that TsmartGP can find more errors while its accuracy is also higher than Cppcheck and Clang Static Analyzer. The demo video is available at https://youtu.be/IQlshemk6OA.
Optimal Foraging Algorithm Based on Differential Evolution

The optimal foraging algorithm (OFA) was proposed by summarizing the rules of the animal foraging behavior in a group. Therefore OFA also has the defects of the swarm intelligence algorithm, such as easy to trap into local optimum and low convergence accuracy. In order to overcome these defects, an optimal foraging algorithm based on differential evolution (DEOFA) is proposed. The differential evolution mechanism contains mutation and crossover operators. The mutation and crossover operators are used to accelerate the convergence speed and global search capability of the OFA. The mutation operator is adopted to perform mutation operations centered on the optimal individual of each iteration to raise the convergence accuracy of the OFA. The test results of 30 benchmark functions show that the performance of DEOFA is better than nine compared algorithms in search accuracy, convergence speed and robustness. In order to verify the effectiveness of the DEOFA in solving practical problems, DEOFA is applied to solve the 0-1 knapsack problem. The test results in the six examples of 0-1 knapsack problems indicate that the DEOFA achieves better performance in accuracy, stability and high dimension.
Globalization - its influences &amp; impacts on specific regions of Africa

Relying on her own practical experience, the author presents the possible ways of opening up job opportunities in some selected regions of developing countries in Africa. The paper will thus cover both merits and demerits of globalization encountered in the countries´ economies and in their social &amp; cultural spheres. Applying previous experience, the text aims to provide theoretical summary of the globalization process and its consequences for the continent. In addition, it highlights the causal relation between unemployment and migration, and shows the disruptive influence that globalization has on the local markets and social &amp; cultural life. In the empirical section of the paper the author submits results of her own combined research that employed both quantitative and qualitative methods. The research, conducted in Uganda.
A Novel Lesion Segmentation Method based on Breast Ultrasound Images

Lesion segmentation is a critical step in computer-aided diagnostic(CAD) systems based on breast cancer imaging. Accurate segmentation directly affects the final determination of the nature of lesion. However, due to the low quality of ultrasound (US) images, lesion segmentation based on US images of breast is challenging. This paper presents an improved marker watershed algorithm for lesion segmentation of breast US images. It uses the efficient and fast curvature filtering(CF) and Gaussian enhancement method to pre-process the image, then creates the segmentation function through Newton filter based on computing gradient of the image, then compares it with the labeling function obtained by binarizing the image to get the most similar parts of them.The intersection is used as the input of the marked watershed(mw) algorithm, and then the candidate boundary is obtained. Finally, the final boundary is determined by maximizing the average radial derivative(ARD) function. The novel method was tested with 400 sets of US images and quantified by using both area and contour error metrics. The result shows that our method can extract the relatively accurate lesion region effectively and efficiently and solves the problem of over-segmentation of the watershed algorithm to some extent. Especially for the segmentation of tumors with internal calcification points or blurred boundaries, it shows better performance.
Preparing an ICT Innovation with Lectora Inspire as Teaching Media for Electrolyte and Non-Electrolyte Solutions for High School Class X

This study aims to determine ICT based Lectora Inspire media as learning media for electrolyte and nonelectrolyte solutions in high schools according to national education standards board.To achieve the objectives, research development was conducted referring to ADDIE modified model.The feasibility of the media was assessed using non-test instrument with likert scale 1-4 by expert validators consist of chemistry lecturers and teachers, and data was processed using descriptive statistics.The results show that an innovative ICT learning media based on lectora inspire can be used for teaching electrolyte and non-electrolyte solution material in high school class X and Innovative ICT based on lectora inspire media for electrolyte and non-electrolyte solutions obtained, had been very feasible score referring to the national education standards board, which indicated by content feasibility 3,66, language feasibilty 3,89 and presentation feasibility 3,94.
A new corpus-based convolutional neural network for big data text analytics

Companies market their services and products on social media platforms with today's easy access to the internet. As result, they receive feedback and reviews from their users directly on their social media sites. Reading every text is time-consuming and resourcedemanding. With access to technology-based solutions, analyzing the sentiment of all these texts gives companies an overview of how positive or negative users are on specific subjects will minimize losses. In this paper, we propose a deep learning approach to perform sentiment analysis on reviews using a convolutional neural network model, because that they have proven remarkable results for text classification. We validate our convolutional neural network model using large-scale data sets: IMDB movie reviews and Reuters data sets with a final accuracy score of ~86% for both data sets.
Connected Chord Diagrams and Bridgeless Maps

We present a surprisingly new connection between two well-studied combinatorial classes: rooted connected chord diagrams on one hand, and rooted bridgeless combinatorial maps on the other hand. We describe a bijection between these two classes, which naturally extends to indecomposable diagrams and general rooted maps. As an application, this bijection provides a simplifying framework for some technical quantum field theory work realized by some of the authors. Most notably, an important but technical parameter naturally translates to vertices at the level of maps. We also give a combinatorial proof to a formula which previously resulted from a technical recurrence, and with similar ideas we prove a conjecture of Hihn. Independently, we revisit an equation due to Arquès and Béraud for the generating function counting rooted maps with respect to edges and vertices, giving a new bijective interpretation of this equation directly on indecomposable chord diagrams, which moreover can be specialized to connected diagrams and refined to incorporate the number of crossings. Finally, we explain how these results have a simple application to the combinatorics of lambda calculus, verifying the conjecture that a certain natural family of lambda terms is equinumerous with bridgeless maps.