Reviewerly/exHarmony huggingface dataset:
Determining the main components of the formation of professional competence for students

The focus of the education system on the assimilation of knowledge, which was traditional and justified before, no longer corresponds to the modern social order, is determined by the objective need of society for a new person, capable of independent, responsible, creative actions, an intellectual person, with a critical thinking, liberated from dogmas, living in a complex. The fulfilment of these tasks requires a significant improvement in the quality of professional training of future specialists, based on the application of the pedagogical aspects of human existence and development in the context of the formation of their professional competence and involves the development of personal qualities and creative abilities, the ability to independently acquire new knowledge and solve problems, and navigate in the life of society.
Huffman-based lossless image encoding scheme

The data produced in today’s Internet and computer world are expanding their wings day by day. With the passage of time, storage and archiving of this data are becoming a significant problem. To overcome this problem, attempts have been made to reduce data sizes using compression methods. Therefore, compression algorithms have received great attention. In this study, two efficient encoding algorithms are presented and explained in a crystal-clear manner. In all compression algorithms, frequency modulation is used. In this way, the characters with the highest frequency after each character are determined and the Huffman encoding algorithm is applied to them. In this study, the compression ratio (CR) is 49.44%. Moreover, 30 randomly selected images in three different datasets consisting of USC-SIPI, UCID, and STARE databases have been used to evaluate the performance of the algorithms. Consequently, excellent results have been obtained in all test images according to well-known comparison algorithms such as the Huffman encoding algorithm, arithmetic coding algorithm, and LPHEA.
Multimodal Hierarchical Attention Neural Network: Looking for Candidates Behaviour Which Impact Recruiter's Decision

Automatic analysis of job interviews has gained in interest amongst academic and industrial research. The particular case of asynchronous video interviews allows to collect vast corpora of videos where candidates answer standardized questions in monologue videos, enabling the use of deep learning algorithms. On the other hand, state-of-the-art approaches still face some obstacles, among which the fusion of information from multiple modalities and the interpretability of the predictions. We study the task of predicting candidates performance in asynchronous video interviews using three modalities (verbal content, prosody and facial expressions) independently or simultaneously, using data from real interviews which take place in real conditions. We propose a sequential and multimodal deep neural network model, called Multimodal HireNet. We compare this model to state-of-the-art approaches and show a clear improvement of the performance. Moreover, the architecture we propose is based on attention mechanism, which provides interpretability about which questions, moments and modalities contribute the most to the output of the network. While other deep learning systems use attention mechanisms to offer a visualization of moments with attention values, the proposed methodology enables an in-depth interpretation of the predictions by an overall analysis of the features of social signals contained in these moments.
Communicating Branching Plans for Human-Agent Decision Making

Recent advances in visualisation technologies have opened up new possibilities for human-agent communication. For systems where agents use automated planning, visualisation of agent planned actions can play an important role in allowing human users to understand agent intent and to help decide when control can be delegated to the agent or when they need to be involved. We are interested in application areas where branched plans are required, due to the typical uncertainty experienced. Our focus is how best to communicate, using visualisation, the key information content of a branched plan. It is important that such visualisations communicate the complexity and variety of the possible executions captured in a branched plan, whilst also connecting to the practitioner's understanding of the problem. Thus we have developed an approach that: generates the complete branched plan, to be able to provide a full picture of its complexity; a mechanism to select a subset of diverse traces that characterise the possible executions; and an interface that uses 3D visualisation to communicate details of these characterising execution traces to practitioners. Using this interface, we conducted a study evaluating the impact of different modes of presentation on user understanding. Our results support our expectation that visualisation of characterising branched plan execution traces increases user understanding of agent intention and range of plan execution possibilities.
Why Citizen's Resist Contact Tracing Apps: Factors affecting Adoption of Aarogya Setu

The COVID-19 forced governments worldwide to deploy contact-tracing apps as an integral part of their lockdown exit strategies. The challenge was mass users’ Adoption of tracing apps deployed. One such app was Aarogya Setu, deployed by the Indian Government. The initiative did not see an exciting response from most of the population in the initial phase. The Government had to change the approach after a while. The current study proposes a conceptual model establishing the theoretical connection among four critical factors: Innovation Resistance, Perceive Security, Perceived Risk, Privacy, and Co-production.
Application of Heterogenous Bagging Ensemble Model for predicting Breast Cancer

Breast cancer is associated with abnormal breast cells emanating from the breast tissues, having the propensity for malignancy or non-malignancy. The causativeness of breast cancer can be linked with genetic or environmental factors. Reliable prediction is integral to proper management and treatment of breast cancer. Sequent to this, researchers have placed a high priority toward enhancing the accuracy for breast cancer prediction. This study employs the rich capability of ensemble bagging machine learning technique for predicting breast cancer. The Heterogenous Bagging Ensemble Model for Predicting Breast Cancer (HBEM-BC) was initiated employing Decision Tree (DT) and Logistic Regression (LR) as base learners. The HBEM-BC was implemented utilizing python programming language with subsequently interfaces presented. The validation of the HBEM-BC presented an accuracy value of 0.74(74%) while independently presenting a Root Square Means Error (RMSE) of 0.41(41%) for Logistic Regression (LR) and 0.51(51%) for Decision Tree (DT) respectively.
Extention of Creative Approaches Visual Artists by Augmented Reality (AR)

There are currently over 3.8 billion smartphone users in the world (Reference from 01.2021). Mobile technologies are becoming an integral part of our lives and this undoubtedly provides a large and diverse environment for the expression of artists from all fields. Mobile technology is changing the way we encounter art. They do it mobile and the term "on demand" is extended to "on the go". The works are becoming more diverse and innovative and every person with a smartphone is a potential user of art. Most modern smart devices are now able to provide Augmented Reality (AR) experiences. AR is already used by innovative brands in trade and marketing, but AR can, and is used, and will be used more and more in the art world. We will no longer associate the visual arts with just visiting art galleries or theater and concert halls. Technologies such as augmented reality (AR), virtual reality (VR), mixed reality (MR) or generalize Extended Reality (XR) open up new creative possibilities for artists and new experiences for the audience. This not only gives new sensations to the audience, but also frees the art from the gallery, thus reaching a completely new audience. Keywords: Augmented Reality (AR), Interactive Art, Visual Art
Watermarking Scheme based on Chinese Remainder Theorem and Integer Wavelet Filters for Copyright Protection

The development of image watermarking schemes has grown rapidly especially in non-block watermarking. However, non-block watermarking produced less robustness against compression and filtering attacks. This study proposed a new model to improve the robustness using a Euclidean distance on the wavelet sub-bands of Integer Wavelet Transform (IWT) with the Chinese Remainder Theorem (CRT). The watermark is then embedded on a low-frequency coefficient according to a predetermined location. CRT is used on the embedding scheme by utilizing integer wavelet filters for decomposing the host images. Both methods are work on signed integer values to avoid the truncation process that usually occurs in the embedding process. The experimental results show that the proposed method produced a better robustness level compared to the CRT method under Gaussian filter, rescaling, cropping, JPEG, and JPEG2000 compression. The proposed scheme achieved high imperceptibility with an average SSIM value of 0.998. The results show that our method can improve the robustness and maintain the visual quality of the watermarked image.
Literary Center of Constructivists on Its Way to the RAPP (From the History of Literary Life at the Turn of the 1920s — 1930s)

The article examines one of the episodes of the literary struggle of the late 1920s — early 1930s — the history of the entry of the Literary Center of Constructivists (LCC) into the Russian Association of Proletarian Writers (RAPP). At the beginning of 1930, almost all literary groups and associations faced the need to define a new level of interaction with RAPP. LCC, as one of the literary groups closest to RAPP, seemed to have all the prerequisites for a successful association with the RAPP. But in reality, this did not happen. Members of RAPP are suspicious of constructivists; attacks at LCC are becoming more frequent in the press. Always considered a left-wing association, LLC is declared a petty-bourgeois group, with which, despite its disbandment, an irreconcilable struggle is required. This article bears upon not only the periodicals of 1930 but also and mainly upon the hitherto unstudied transcripts and other archival documents of RAPP. New archival materials reveal internal processes of the literary struggle at the turn of the decade, and make it possible to demonstrate how, even after the acceptance of the Constructivists by RAPP, the former continue to be perceived as a hostile group whose past was to always blame them.
A Method of Estimating the Future Meteorological Data

In order to plan projects dependent on weather conditions, it is important to estimate meteorological parameters in order to meet the timetable required. In this study different methods for estimation of distribution for meteorological data is proposed using historical, according to location; Mehr-Abad airport meteorological, parameters used are; wind speed at a location, ambient temperature at the location, air pressure at the location, air humidity at the location, Cloudiness at the location, precipitation (mm) at the location, one by one.
Understanding the Necessary Conditions of Multi-Source Trust Transfer in Artificial Intelligence

Trust transfer is a promising perspective on prevalent discussions about trust in AI-capable technologies.However, the convergence of AI with other technologies challenges existing theoretical assumptions.First, it remains unanswered whether both trust in AI and the base technology is necessary for trust transfer.Second, a nuanced view on trust sources is needed, considering the dual role of trust.To address these issues, we examine whether trust in providers and trust in technologies are necessary trust conditions.We conducted a survey with 432 participants in the context of autonomous vehicles and applied necessary condition analysis.Our results indicate that trust in AI technology and vehicle technology are necessary sources.In contrast, only vehicle providers represent a necessary source.We contribute to research by providing a novel perspective on trust in AI, applying a promising data analysis method to reveal necessary trust sources, and consider duality of trust in trust transfer.
Innovative approaches in the organization of independent work of applicants for higher education in the conditions of information educational environment

The article considers the organization of independent work as the main means of forming competencies largely depends on the extent to which the teacher has modern educational technologies, including in the field of self-education. It was found that the tasks of competence formation cannot be solved within one discipline, by the efforts of one or more teachers: a systematic approach of all teachers is required.
Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms

When it comes to solving optimization problems with evolutionary algorithms (EAs) in a reliable and scalable manner, detecting and exploiting linkage information, i.e., dependencies between variables, can be key. In this article, we present the latest version of, and propose substantial enhancements to, the Gene-pool Optimal Mixing Evoutionary Algorithm (GOMEA): an EA explicitly designed to estimate and exploit linkage information. We begin by performing a largescale search over several GOMEA design choices to understand what matters most and obtain a generally best-performing version of the algorithm. Next, we introduce a novel version of GOMEA, called CGOMEA, where linkage-based variation is further improved by filtering solution mating based on conditional dependencies. We compare our latest version of GOMEA, the newly introduced CGOMEA, and another contending linkage-aware EA, DSMGA-II, in an extensive experimental evaluation, involving a benchmark set of 9 black-box problems that can only be solved efficiently if their inherent dependency structure is unveiled and exploited. Finally, in an attempt to make EAs more usable and resilient to parameter choices, we investigate the performance of different automatic population management schemes for GOMEA and CGOMEA, de facto making the EAs parameterless. Our results show that GOMEA and CGOMEA significantly outperform the original GOMEA and DSMGA-II on most problems, setting a new state of the art for the field.
Chemistry textbooks as a means of supporting cognitive activity of general secondary education students

The article is devoted to suchnew direction of research as creating textbooks that provide competency-based approach to teaching Chemistry. A brief description of the results of the analysis of scientific literature on the research topic is presented. Based on them, it was concluded that it is necessary to develop a concept for creating Chemistry textbooks that would allow realizing the competence potential of academic subject. The main pro-visions of the author's concept are stated. According to them, the textbook should equally present its content and procedural components, which can ensure the formation of students’ key and subject competencies. The author’s rubrication of the textbooks is presented: “After mastering the material of the unit, you will be able...”, “Remember! Pay attention!”, “Chemistry is life: a naturalist’s page”, “Briefly about the main thing”, “Erudite page”, “Work in groups”, “We work with media sources”, “We know, we understand”, and “We apply”. The design of the units is briefly described. The embodiment of the conceptual provisions in school Chemistry textbooks for grades 8–11 created by the author is revealed on specific examples.
Deep NLP Explainer: Using Prediction Slope to Explain NLP Models

Natural Language Processing models have been increasingly used for many tasks, from sentiment analysis to text summarization. Most of these models are reaching the performance of human experts. Unfortunately, not only are these models not intuitive to the end-user, but they are also not even interpretable to highly-skilled Machine Learning scientists. We need explainable artificial intelligence to be able to trust models in high-stakes scenarios, and also to develop insights to optimize them by removing existing limitations and biases. In this paper, we devise a new tool called “Prediction Slope” that can be applied to any NLP model, extracting the importance rate of the component words and thereby helping to explain the model. It uses the average effect each word has on the final prediction slope as the word importance rate. We compared our technique with preceding approaches and observed that although they perform similarly, the earlier approaches do not generalize as well. Our method is independent of the model’s architecture and details.
Wavelet neural network for big data analytics in banking via GPU

Big data is hard to process using conventional technologies and hence calls for massively parallel processing. Machine learning techniques have been widely adopted in several massive and complex data-intensive fields for handling large data. Artificial neural networks (ANNs) are the most common machine learning techniques used for classification, function approximation, dimensionality reduction etc. Wavelet neural network (WNN) is one of them. The architecture of WNN is having a lot of matrix computations, which can be parallelized by GPU. Theano is used as a programming model for accelerating general-purpose workloads. In our work, we implemented the WNN using Theano. The efficacy of WNN is tested on various bank datasets. In this process, the performance of a conventional CPU implementation of WNN was tested with that of GPU, and the latter was found to be much faster on all datasets.
Automatic Tracking Method of Basketball Flight Trajectory Based on Data Fusion and Sparse Representation Model

The appearance model of flying basketball obtained by the traditional basketball flight trajectory tracking method is not accurate, which leads the anti-interference performance of trajectory tracking not ideal. Based on data fusion and sparse representation model, a new automatic trajectory tracking method is proposed. Firstly, the relevant technologies of basketball flight trajectory automatic tracking are studied and summarized, and then the method is studied. The specific implementation steps of this method are as follows: the features of flying basketball images were extracted by the target feature extraction algorithm, and the appearance model of flying basketball was built based on sparse representation. Data fusion technology and particle filter algorithm are combined to realize automatic tracking of basketball flight path. Through three axial basketball trajectories of automatic tracking test and noise test and verify the design method under the 3D world coordinate system to achieve the X, Y, and Z axis up more accurate tracking, at the same time, after applying measurement signal to noise, automatic trajectory tracking results affected by some, but still managed to realize the trajectory tracking.
Recognition of printed small texture modules based on dictionary learning

Abstract Quick Response (QR) codes are designed for information storage and high-speed reading applications. To store additional information, Two-Level QR (2LQR) codes replace black modules in standard QR codes with specific texture patterns. When the 2LQR code is printed, texture patterns are blurred and their sizes are smaller than $$0.5{\mathrm{cm}}^{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0.5</mml:mn> <mml:msup> <mml:mrow> <mml:mi>cm</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> </mml:math> . Recognizing small-sized blurred texture patterns is challenging. In original 2LQR literature, recognition of texture patterns is based on maximizing the correlation between print-and-scanned texture patterns and the original digital ones. When employing desktop printers with large pixel extensions and low-resolution capture devices, the recognition accuracy of texture patterns greatly reduces. To improve the recognition accuracy under this situation, our work presents a dictionary learning based scheme to recognize printed texture patterns. To our best knowledge, it is the first attempt to use dictionary learning to promote the recognition accuracy of printed texture patterns. In our scheme, dictionaries for all kinds of texture patterns are learned from print-and-scanned texture modules in the training stage. And these learned dictionaries are employed to represent each texture module in the testing stage (extracting process) to recognize their texture pattern. Experimental results show that our proposed algorithm significantly reduces the recognition error of small-sized printed texture patterns.
Teachers’ Knowledge and Practices in Using Digital Literacy in Enhancing Communicative Competence in the English as a Second Language Class

This study explored teachers’ knowledge and practices in using digital literacy in enhancing the communicative competence of English as a second language (ESL) learners. This is because there are different types of data in the digital domain, which necessitate their use in various fields of learning. One hundred teachers of English from South-west Nigeria participated in a survey through online platforms. After analyzing the data through both descriptive and inferential statistics, the major findings revealed the following: (1) ESL teachers have maximum knowledge of the existence of many of the existing digital tools (2) they largely perceive that digital literacy can be used to enhance communicative competence, but do not maximally use the digital tools to do so, (3)the digital divide is a major factor limiting teachers’ use of digital tools in enhancing learner communicative competence. In view of this, it is therefore recommended,firstly, that teachers be given adequate training on the use of digital tools in enhancing the communicative competence of ESL learners. Secondly, the government and other stakeholders should invest more in technology particularly in the area of education. Lastly, ESL teachers should make conscientious efforts to deploy digital tools in teaching to enhance the communicative competence of the learners.
Segmented Approach for the Prony's Method Numerical Realization

In the current paper we propose a modified approach to the eigenvector method for the solution of the Prony's method. The proposed approach requires the segmentation of the input signal into parts that may overlap. The Prony's method is then applied for each individual part. The mean or median estimation of the true values can be applied either to the eigenvector itself, or to the final values of complex amplitudes and frequencies. All possible combinations of the above-mentioned approaches were tested for the current paper. Results of the estimation of the amplitudes and frequencies were analyzed separately from the results of initial signal reconstruction. The proposed method was compared with the standard Kumaresan-Tufts approach and standard eigenvector approach.