Reviewerly/exHarmony huggingface dataset:
CYBERSECURITY AND INNOVATIVE DIGITAL EDUCATIONAL ENVIRONMENT

The article considers interaction of innovation, cybersecurity, and digital education environment. It has been demonstrated that comprehensive digitalization of society is being accompanied by changes in the innovative potential and educational needs of Ukraine, especially in the development of the human intellectual capital and its protection in the digital environment. Human capital in innovation plays a significant role, especially in the digital age. Young people are considered as a vulnerable group that could be the main goal of cyber cognitive operations and as the weakest link of the System. The analysis of the Global Innovation Report has revealed that the real level of digitalization in Ukraine does not match its actual human potential and creative outputs. Ukraine has faced gaps in the triangle "human capital (potential) - technological support of innovations - creative activity" that should be eliminated to increase the national Global Innovation Index (GII). Human talent and competence (as a capital) are developed in the learning process. It has been noted that a joint project is the weakest link in terms of intellectual capital and the intellectual property right. Personal information about the project competitors is usually not defended. The issues of innovation in the digital learning environment exacerbate the issues of cybersafety of the education process participants.The traditional cybersecurity approach is unable to detect and tackle new sophisticated attacks. The authors have developed a general model of cyber threats in the field of education and mitigation of their impact.The new strategy in cybersecurity is proposed: not only prompt defence, but threat hunting and resilience training of users. Threat hunting can be presented by two approaches: threat detection (reactive) and threat discovery (proactive), as components of the proposed model. The resilience training of users is associated with threat awareness and special cybersafety training. A general model of cyber threats in the field of education and ways to avoid them is presented. Special attention should be paid to the strengthening of the role of media in educational processes in digital environment, as a current area of cyber hazards. The role of media in stimulating horizontal communication in the educational process is growing. Participants in the digital environment are confidently turning from passive consumers to prosumers, and the media are becoming an information intermediary with a double function: a platform for providing access and information exchange in the educational process and technological tools for joint creation of products (creative commons).
Database Management Module Framework for Dynamic Contingency Analysis and Visualization

The Dynamic Contingency Analysis Tool (DCAT) is being developed to mimic the response of generation dynamics, protection, and load components to extreme contingencies in power systems. Currently, DCAT generates a huge data set, with the analysis results stored in multiple subfolders using a great many files in different formats (power flow cases, time series, figures, spreadsheets). To help power system engineers thoroughly understand and analyze system behavior under many scenarios and contingencies, we developed a database management module (DBM) that enables future integration of DCAT with industry-grade Big Data analytical and interactive visualization platforms. In this paper, we demonstrate the DBM framework with interactive analytics and visualization for DCAT on the synthetic South Carolina 500-bus power system model using hurricane contingencies that capture possibilities for an extreme contingency. DCAT's advanced data management and interactive visualization can help planning engineers to visually explore and analyze system behavior, all in one view.
IIITG-ADBU at SemEval-2020 Task 8: A Multimodal Approach to Detect Offensive, Sarcastic and Humorous Memes

In this paper, we present a multimodal architecture to determine the emotion expressed in a meme.This architecture utilizes both textual and visual information present in a meme.To extract image features we experimented with pre-trained VGG-16 and Inception-V3 classifiers and to extract text features we used LSTM and BERT classifiers.Both FastText and GloVe embeddings were experimented with for the LSTM classifier.The best F1 scores our classifier obtained on the official analysis results are 0.3309, 0.4752, and 0.2897 for Task A, B, and C respectively in the Memotion Analysis task (Task 8) organized as part of International Workshop on Semantic Evaluation 2020 (SemEval 2020).In our study, we found that combining both textual and visual information expressed in a meme improves the performance of the classifier as opposed to using standalone classifiers that use only text or visual data.
A Study on the Improvement of Law for the on-board Training of Maritime Schools by COVID-19

Under the recent worldwide spread of COVID-19, the maritime schools need to review the mandatory minimum requirements for issuing a certification of officers through the suspension and resumption of on-board training. The domestic law requires at least one year of sea-going services for graduates of maritime schools to issue a certification of officers, and requires at least six months of on-board training during their sea-going services. However, STCW allows online education methods such as e-learning by approval of the competent authority for one year
Random-access-aware Light Field Video Coding using Tree Pruning Method

The increasing prevalence of VR/AR as well as the expected availability of Light Field (LF) display soon call for more practical methods to transmit LF image/video for services. In that aspect, the LF video coding should not only consider the compression efficiency but also the view random-access capability (especially in the multi-view-based system). The multi-view coding system heavily exploits view dependencies coming from both inter-view and temporal correlation. While such a system greatly improves the compression efficiency, its view random-access capability can be much reduced due to so called "chain of dependencies." In this paper, we first model the chain of dependencies by a tree, then a cost function is used to assign an importance value to each tree node. By travelling from top to bottom, a node of lesser importance is cut-off, forming a pruned tree to achieve reduction of random-access complexity. Our tree pruning method has shown to reduce about 40% of random-access complexity at the cost of minor compression loss compared to the state-of-the-art methods. Furthermore, it is expected that our method is very lightweight in its realization and also effective on a practical LF video coding system.
Innovation and Human Development Perspectives in West Africa

Innovation during this era of globalization plays a crucial role in the growth and development of an economy. This study empirically investigates the role of research and development (innovation) on human development in West Africa (2004—2014). The estimation techniques utilized to carry out the objective are the standard variations of the Generalized least squares—Panel Fixed and Panel Random effects estimation techniques. The empirical results show that research and development in West Africa has a statistically significant positive impact on human development which emphasizes the potentials of human development to be harnessed by consistent research and development. The study recommends increased research and development (innovation) through adequate research and development funding and university-industry partnership for real human development problems to be solved.
Analysis on the Construction of College English Teaching Evaluation System Based on Online Learning Platform

With the rapid development of network information technology, it has been widely used in various fields. Of course, in the field of education is no exception. In college English teaching, the application of online learning platform can effectively improve the quality of college students’ English learning and teaching effect. Starting from the importance of the construction of college English teaching evaluation system based on online learning platform, this paper puts forward the measures for the construction of college English teaching evaluation system for reference.
Towards Shrewd Object Visualization Mechanism

In order to measure the accurate outcome of different visualization mechanism it is imperative to adopt a shrewd strategy.Indeed, the outcome of experiment is focusing to assess the value of complex visualization approaches when comparing with alternative methods for data analysis.The interaction between participant prior knowledge and experience, a diverse range of experimental or real -world data sets and a dynamic interaction with the display system presents challenges when seeking timely, affordable and statistically relevant results.A hybrid approach proposed is being proposed to deal with complex interactive data analysis tools.This approach involves a structured survey completed after free engagement with the software platform by expert participants.The survey captures objective and subjective data points relating to the experience with the goal of making an assessment of the software performance supported by statistically signifi cant experimental results.This work is particularly applicable to fi eld of network analysis for cyber security and also military cyber operations and intelligence data analysis.
A Preliminary Look at Heuristic Analysis for Assessing Artificial Intelligence Explainability

Artificial Intelligence and Machine Learning (AI/ML) models are increasingly criticized for their “black-box” nature. Therefore, eXplainable AI (XAI) approaches to extract human-interpretable decision processes from algorithms have been explored. However, XAI research lacks understanding of algorithmic explainability from a human factors’ perspective. This paper presents a repeatable human factors heuristic analysis for XAI with a demonstration on four decision tree classifier algorithms.
Book spine recognition with the use of deep neural networks

Nowadays deep neural networks play a significant part in various fields of human activity. Especially they benefit spheres dealing with large amounts of data and lengthy operations on obtaining and processing information from the visual environment. This article deals with the development of a convolutional neural network based on the YOLO architecture, intended for real-time book recognition. The creation of an original data set and the training of the deep neural network are described. The structure of the neural network obtained is presented and the most frequently used metrics for estimating the quality of the network performance are considered. A brief review of the existing types of neural network architectures is also made. YOLO architecture possesses a number of advantages that allow it to successfully compete with other models and make it the most suitable variant for creating an object detection network since it enables some of the common disadvantages of such networks to be significantly mitigated (such as recognition of similarly looking, same-color book coves or slanted books). The results obtained in the course of training the deep neural network allow us to use it as a basis for the development of the software for book spine recognition.
The 19th Top Chess Engine Championship, TCEC19

TCEC19 was the 18th season of the Top Chess Engine Championship and ran from August 6th to October 16th, 2020. TCEC has become the largest Open Computer Chess Championship. It attracts the best engines in the field and provides an opportunity for a comparative analysis of the Shannon-AB and the new Neural-Network engines’ styles of play. STOCKFISH retained the Grand Champion title with +9 in another exciting Superfinal, but only because it took on 'NN' technology 'just in time'. These two engines have dominated the field for the last eight TCEC seasons now. The attached files at http://centaur.reading.ac.uk/93815 provide the 726 games with engine PVs, the detail on them, some summary statistics and the completion to mate of the wins. Bonus matches put these two engines' performance in a valuable context of STOCKFISH_CLASSICAL and KOMODO.
Functions of Participants in the Collaborative Solution of Thinking Problems

The article presents the results of an empirical study of the collaborative solution of thinking problems by groups of students. The study was conducted in the context of educational activities when students solve educational problems. The student group was divided into subgroups of four people; each subgroup was given a learning task. In accordance with the author’s ideas, collaborative thinking is carried out through the implementation of the following functions: generation, selection, meaning transfer, implementation. These functions are distributed among participants in collaborative problem solving. The purpose of the study was to study the distribution of functions when students collaboratively solve thinking problems. To study the distribution of functions, the author used a questionnaire to determine the distribution of functions between students. The author has shown that in the process of collaborative problem solving, functions are distributed among the participants. It is revealed that in the processes of collaborative solution of thinking tasks, each group of students has a unique combination of functions, which the author defines as the group’s thinking style.
Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT

Most current extractive summarization models generate summaries by selecting salient sentences.However, one of the problems with sentence-level extractive summarization is that there exists a gap between the human-written gold summary and the oracle sentence labels.In this paper, we propose to extract fact-level semantic units for better extractive summarization.We also introduce a hierarchical structure, which incorporates the multi-level of granularities of the textual information into the model.In addition, we incorporate our model with BERT using a hierarchical graph mask.This allows us to combine BERT's ability in natural language understanding and the structural information without increasing the scale of the model.Experiments on the CNN/DaliyMail dataset show that our model achieves state-of-the-art results.
Educational Innovation through ICT-Mediated Teaching Strategies in the Initial Teacher Education of English Language Teachers

This paper describes the results of the first cycle of a co-joint research study in methodological innovation in the initial education of pre-service language teachers. The experience consisted of the implementation of several learning strategies mediated by different ICT tools. These strategies were used by three teachers, in three different academic spaces of a B.A. in Spanish and Foreign Languages during the second semester of 2018. The purpose of this implementation was to examine the value of ICT-mediated learning strategies and gaining insights into how to integrate ICTs with a process of pedagogical and methodological innovation. The results of the data analysis, which were collected through a qualitative-quantitative survey, indicate that the integration of strategies mediated by ICTs seems to be essential for methodological and pedagogical innovation because they promote the development of collaborative and cooperative learning, foster autonomy and self-directed learning. Besides, these strategies also provide a range of possibilities for bolstering the development of the capacity of agency pre-service teachers.
Binary Analysis with Architecture and Code Section Detection using Supervised Machine Learning

When presented with an unknown binary, which may or may not be complete, having the ability to determine information about it is critical to future reverse engineering, particularly in discovering the binary's intended use and potential malicious nature. This paper details techniques to both identify the machine architecture of the binary, as well as to locate the important code segments within the file. This identification of unknown binaries makes use of a technique called byte histogram in addition to various machine learning (ML) techniques, which we call “What is it Binary” or WiiBin. Benefits of byte histograms reflect the simplicity of calculation and do not rely on file headers or metadata, allowing for acceptable results when only a small portion of the original file is available; e.g., when encrypted and/or compressed sections are present in a binary. Utilizing WiiBin, we were able to accurately (>80%) determine the architecture of test binaries with as little as a 20% contagious portion of the file present. We were also able to determine the location of code sections within a binary by utilizing the WiiBin framework. Ultimately, the more information that can be gleaned from a binary file, the easier it is to successfully reverse engineer.
Entropy measurement of ordered patterns in neuronal network with repulsive coupling

Traveling waves, standing waves, and spiral waves occur spontaneously in the brain neural network in some brain states. The occurrence of these ordered spatiotemporal patterns is often related to some neurological diseases. However, the mechanisms behind the generation of the ordered pattern are not fully understood. How to quantitatively describe the nature of these spatiotemporal patterns still needs further exploring. In order to solve these problems, the Hindmarsh-Rose neuron model is used to study the dynamic behavior of the two-dimensional (2D) neuronal network with double-coupling layer, which is composed of nearest-neighbor excitatory coupling and long-range repulsive coupling layers and evolves from an initial state with a random phase distribution. An improved cluster entropy is proposed to describe the spatiotemporal pattern of the neuronal network. The numerical simulation results show that the repulsive coupling can either promote the formation of ordered patterns or suppress the formation of ordered patterns. When the repulsive coupling strength and excitatory coupling strength are appropriately selected, the chaotic network can spontaneously generate single spiral wave, multiple spiral wave, traveling wave, the coexistence of spiral wave and others wave state, the coexistence of target wave and others wave state, the coexistence of traveling wave and standing wave, etc. The probability with which spiral wave and traveling wave occur reach 0.4555 and 0.1667 respectively. The probability with which target wave and other states co-occur, and the probability with which the traveling wave and the standing wave co-occur, are 0.0389 and 0.1056, respectively. These ordered wave patterns and chaotic states can be distinguished by using the proposed cluster entropy. When the repulsive coupling strength is large enough, the neuronal network is generally in chaotic state. It is found by calculating cluster entropy that a large cluster can appear in the neuronal network when the excitatory coupling strength and repulsive coupling strength are both weak. These results can conduce to understanding the self-organization phenomena occurring in the experiments and also to treating various neurological diseases.
Verification of pattern unlock and gait behavioural authentication through a machine learning approach

Purpose The existing authentication procedures (pin, pattern, password) are not very secure. Therefore, the Gait pattern authentication scheme is introduced to verify the own user. The current research proposes a running Gaussian grey wolf boosting (RGGWB) model to recognize the owner. Design/methodology/approach The biometrics system plays an important role in smartphones in securing confidential data stored in them. Moreover, the authentication schemes such as passwords and patterns are widely used in smartphones. Findings To validate this research model, the unauthenticated user's Gait was trained and tested simultaneously with owner gaits. Furthermore, if the gait matches, the smartphone unlocks automatically; otherwise, it rejects it. Originality/value Finally, the effectiveness of the proposed model is proved by attaining better accuracy and less error rate.
The listening skill of autistic students in learning English through total physical response

This study analyses how a teacher at SMPLB-CD YPAC (Foundation for Children with Disabilities), Banda Aceh, Indonesia, teaches English listening using the Total Physical Response (TPR) method to autistic students. In this method, a teacher instructed the students to act as modelled by the teacher. Students are considered to have good listening comprehension if they understood the teacher’s commands and imitated the act. This research is qualitative to describe the application of the TPR method in teaching English listening skills to autistic students. The sample for this study was four autistic students at the school under study. The data were collected by observing and documenting the English listening teaching-learning process in the classroom using the TPR method. The data were then analysed, focusing on the classroom dialogues directly related to using the TPR method to teach listening comprehension to autistic students. The results showed that the TPR method is suitable to teach students with autism to learn and understand new words. This was because they were asked to imitate the words verbally and in action repeatedly. The students could memorize the new vocabulary because it involved motor activities, which created a fun atmosphere in the classroom. Students were active in doing the actions and have no pressure to learn. The TPR method also contains elements of games which is able to raise a positive mood in autistic students.
Investigating Societal Biases in a Poetry Composition System

There is a growing collection of work analyzing and mitigating societal biases in language understanding, generation, and retrieval tasks, though examining biases in creative tasks remains underexplored. Creative language applications are meant for direct interaction with users, so it is important to quantify and mitigate societal biases in these applications. We introduce a novel study on a pipeline to mitigate societal biases when retrieving next verse suggestions in a poetry composition system. Our results suggest that data augmentation through sentiment style transfer has potential for mitigating societal biases.