Reviewerly/exHarmony huggingface dataset:
Textbooks for primary school in the reception of Western Ukrainian teachers (the second half of the XIX – early XX centuries)

The process of creating school textbooks is continuous and is based on positive retrospective experience and modern demands of society to the content of education. Under such conditions, the study of the attitude of practising teachers and scholars towards textbooks for primary school in the second half of the nineteenth – early twentieth centuries is relevant for pedagogical science. The article analyses the ideas of Western Ukrainian teachers of the second half of the XIX - early XX centuries on the problem of compiling and using school textbooks, their role in education. The analysis of positive and negative characteristics of textbooks for primary school of the period under study is carried out. The main socio-economic and political factors influencing the state and functioning of textbooks for primary school are considered. The aim of the article is to analyse publications and ideas of Western Ukrainian teachers and scholars towards the structure of textbooks for primary school, scientific and accessible content, brevity, accuracy and quality of presentation, the language of textbooks, clarity of definitions, unambiguous use of terms. The research methodology involved: bibliographic search, comparative analysis, systematization and theoretical generalization, content analysis for analytical processing of literature. During the period under study, theoretical substantiation of textbooks for primary school structure, content, didactic principles of text construction began. The importance of visual instruction in primary school was emphasised. Numerous examples have shown the fact that teachers' dissatisfaction with school textbooks was due to excessive volume, numerous unsuccessful passages of texts with dry content, complex, unsuitable for the children’s style, terms and long sentences. Their main flaw was that the material was presented in a dry and tendentious way, thus creating obstacles to the development of interest in independent reading outside the school.
An Investigation of Automated Design of VMAF

Video quality assessment (VQA) methods have many applications in compression technologies, video transmission planning, and monitoring quality of service. Netflix has recently developed an open-source, state-of-the-art VQA method called video multi-methods assessment fusion (VMAF) which proved to correlate well with the human judgments of perceptual quality of videos. VMAF is designed manually by human experts. To our knowledge, no prior study has been conducted to automate the design of VMAF. In this paper, we propose a simulated annealing search (SA) and a genetic algorithm (GA) to search the space of VMAF configurations. We used the Netflix public dataset and the LIVE-VQA dataset to evaluate the proposed approaches. It is found that both SA and the GA improve the design of VMAF with the GA being the superior method.
Greedy Channel Balanced Routing Strategy of Payment Channel Networks

Restricted by the strict consensus process, blockchain suffers from low performance. The Payment Channel Networks (PCNs) is an optimized solution for efficient transactions on the blockchain which can improve the performance of blockchain. PCNs offers off-chain settlement of transactions with minimal involvement of expensive blockchain operations. In this paper, we jointly consider node privacy and network imbalance issues and investigate the problem of payment routing in PCNs. We propose a greedy channel balanced routing strategy of PCNs called GCBRS_PCNs. GCBRS_PCNs divides a transaction into multiple transaction units, and greedily selects the transaction path for each transaction unit which uses the node Gini index as the selection standard of the next hop. This method realizes the local equilibrium of the network while ensuring the node privacy. The simulations have shown that our proposed mechanism is able to achieve outstanding performance.
The User Satisfaction with the Quality of Library and Information Services: Research Results

The purpose of the study is to analyze the satisfaction of users with library and information services of the Scientific Library of Tuva State University. The main criterion of satisfaction with services is quality. Relatively large number of methods has been developed to study the quality of services provided. It is advisable to use various tools to obtain reliable and complete information about the quality of library work. In the practical activities of the Scientific Library of Tuva State University, there is most widely used statistical analysis of performance indicators, in particular, access to library resources, attendance (the library, the website, electronic library systems, etc.). To study satisfaction with the services provided by the Scientific Library of Tuva State University, there was conducted the user survey, which revealed strengths and weaknesses, as well as allowed to avoid problems in terms of customer service during the distance-learning period. Sociological research of the Scientific Library of Tuva State University revealed the need for advanced training courses for library staff in a number of areas, to create a welcoming library environment, to change the interface of the library website, as well as the need to redesign the training programs for library specialists.
Improving Diversity in Concept Drift Ensembles

This research investigates how the performance of ensembles is influenced by the diversity and behavior of base learners on abrupt and gradual concept drifts. To accomplish that, we propose to enhance ensemble algorithms by adjusting the voting procedure and using heterogeneous base-learners on three benchmark data stream methods: Fast Adaptive Stacking of Ensembles (FASE), Online Adaptive Classifier Ensemble based on bagging (OACE <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BA</inf> ), and Online Adaptive Classifier Ensemble based on boosting (OACE <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BO</inf> ). These ensembles were selected because they use different voting schemes (stacking, simple majority, and weighted voting) to perform the classification. The modified methods, MFASE, MOACE <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BA</inf> and MOACE <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BO</inf> , were compared to their original versions regarding accuracy. Naïve Bayes (NB) and Hoeffding Tree (HT) were used as base learners to evaluate the methods on artificial and real-world datasets. The experimental investigation included 44 experiments and their evaluation using the Friedman and Nemenyi statistical tests. They showed that the modified methods improve the ensemble performance in concept drift problems in many scenarios.
A Family of Turn Based Strategy Games with Moose

The game that is the focus for this study is a simple turn-based strategy game used for a competition in an AI class at Innopolis University for two years. In this study we continue to investigate the game with evolved game-playing agents. The space of the moose competition games is expanded and the hypothesis that changing the resource profiles of the game will lead to the evolution of different types of agents is tested. The fields that the moose forage in may have enhanced rate of plant growth or enhanced richness of plant biomass. Increasing these parameters is found to decrease conflict between moose, after a minimum level is passed, and enhancing richness is found to modify moose behavior in intuitive ways. In some parts of the resource model space, the agents exhibit negative density dependent selection that increases the rate of confrontation. This study demonstrates that the family of moose games provides a rich palette of different games for testing students and agent AIs.
CARM: A Blockchain-based Content Quality Assessment and Rewarding Mechanism

Content Quality Assessment and Rewarding mechanisms are posited as critical variables to improve content quality standards and compensate high-quality contributors. Content authenticity, quality, and relevancy appear to be among the more significant challenges with traditional content platforms. A novel trend in the technology space known as blockchain has been found to address these challenges. The consensus protocols associated with blockchain provide many benefits over traditional mechanisms on the frontiers of privacy, security, and decentralization. However, while these protocols provide consensus on rewards and transactions, they do not fully address content quality. More specifically, the issues around equal voting power are often not entirely consistent regarding voting weight. In this paper, a novel Content Quality Assessment and Rewarding Mechanisms (CARM) is proposed to address the variables of content quality. The system uses CQ Score, a Content Quality Assessment Framework via the Ethereum Blockchain and Smart Contracts to ensure that user variables such as completeness, authenticity, and relevancy are used to qualify content quality.
Global Optimization: A Hybrid Social Spider-Prey and Kestrel-Based Search Strategy in Multi-dimensional Search Space

The social spider-prey (SSP) search is one of the newly developed search strategies for an optimization problem. SSP mimics the behaviour of the spider and prey on the spider’s web. This paper aims to propose a hybrid algorithm that avoids parameter estimation based on trial-and-error for the global optimization problem. The methodology is based on the Kestrel-based search method (KSA) to generate the best weight value of the prey in any hyper-dimensional space, which is constrained by parameters due to the nature and complexity of the problem. The proposed hybrid SSP-KSA was tested on benchmark functions through a computational experiment and the results are discussed. The results indicate that the Hybrid SSP-KSA demonstrate global optimization performance for the different dimensional waves in the search spaces. The results also show the different values of amplitude, and none reached −16 × 10−8 value. Also, the optimal value for the superimposed wave was between 0.1 and 0.13. In conclusion, irrespective of an increase in dimension space, the graph of amplitude converges to optimality which suggests a prey has finally been caught on the spider’s web.
Research on Low Resource Neural Machine Translation Based on Non-autoregressive Model

Abstract The autoregressive model can’t make full use of context information because of its single direction of generation, and the autoregressive method can’t perform parallel computation in decoding, which affects the efficiency of translation generation. Therefore, we explore a non-autoregressive translation generation method based on insertion and deletion in low-resource languages, which decomposes translation generation into three steps: deletion-insertion-generation. Therefore, the dynamic editing of the translation can be realized in the iterative updating process. At the same time, each step can be calculated in parallel, which improves the decoding efficiency. In order to reduce the complexity of data sets in non-autoregressive model training, we have trained Uyghur-Chinese training data with sequence-level knowledge distillation. Experiments on Uyghur-Chinese, English-Romanian distilled data sets and standard data sets verify the effectiveness of the non-autoregressive method.
Perceptions of Faculty Members in Southern Jordan Faculties about the Effectiveness of Electronic Educational Platforms in Developing e-learning Learning Skills

The present study mainly aimed to determine the Perceptions faculty members in the colleges of Southern Jordan, the effectiveness of electronic educational platforms in developing e-learning skills. The study depended on the descriptive analytical approach. Besides, the study community included faculty members working in the faculties of southern Jordan. The sample of the study was (156) male as well as female teachers, they were chosen randomly. Furthermore, the study’s results reflected that the effectiveness of electronic educational platforms for faculty members in developing electronic science skills recorded a positive level. Besides, it recorded an arithmetic mean of (3.59). Moreover, the study came to its end by recommending the necessity of holding a number of courses as well as workshops for faculty members as well as students, to develop their attitudes about the electronic educational platforms’ use in e-learning, and to train them on how to use it, and to modify practical and theoretical curricula of faculties to conform to their application, by using electronic platforms of education, and to dissolve all obstacles until reaching the platforms’ use. E-education, concerning providing fast modern and virtual internet laboratories in faculties in proportion to the students’ number as well as courses.
Hierarchical Open-Set Recognition for Automatic Target Recognition

Traditional classification and recognition models are trained with an assumption that all possible classes are encountered during model training. When such models operate in the open-world, they can encounter observations from previously unknown classes, and are prone to making erroneous high-confidence predictions. In this work, we define the problem of hierarchical Open-Set Recognition (OSR) which seeks classification models that are able to identify if an observation belongs to an unknown class, and to provide information about how the unknown class relates to the known classes. As part of this work we introduce three novel approaches to hierarchical OSR, and a novel evaluation metric that conveys the ability of hierarchical OSR models to provide partial information for predictions. Through empirical results we demonstrate that one of our novel hierarchical OSR approaches outperforms competing methods. Finally, we introduce a use case that shows the benefit of hierarchical OSR for Automatic Target Recognition applications.
Self-Organizing Transformations for Automatic Feature Engineering

Feature Engineering (FE) consists of generating new, better features to improve Machine Learning models. Very often, FE is performed in a series of trial-and-error steps conducted manually by data scientists. Moreover, FE requires data-specific and domain knowledge, both rarely easy to acquire. To alleviate these problems, we propose the Self-Organizing Automatic Feature Engineering (SOAFE), a novel approach for Automatic Feature Engineering (AFE). Different from the majority of the existing AFEs, SOAFE employs an unsupervised technique (Self-Organizing Maps) to identify patterns in the data, and apply a form of cooperative training, inspired by Generative Adversarial Networks, to improve the feature construction. Our results on several datasets show that SOAFE can improve classification models when compared with existing AFE approaches.
Abnormal Working Conditions Judgment Based on Data Sharing System for Power Distribution Station Area

As important components of the Ubiquitous Power Internet of Things, cloud and edge nodes play key technical support roles for its construction. However, data resources stored in them have the characteristics of diverse structure and huge scale. There is an urgent need to improve the efficiency of data sharing services and decision-making responses. This paper builds a data sharing system among cloud and edge nodes to realize information & business collaboration which can promote rapidly responding to advanced services at edge nodes. Based on this system, a method of judging abnormal working conditions in the power distribution station area is proposed. The cloud will use machine learning methods to train the characteristic data and obtain the judgment model. Meanwhile, the edge nodes will judge the abnormal working condition on-site according to the model. It can provide support for the improvement of the regional autonomy and intelligence level of the power distribution station area.
Height Estimation of Atmospheric Reentry of Vehicle Tracked With LFM Waveforms

Conditions for the moment of atmospheric reentry of vehicles tracked with chirp radar are studied in this paper. A Kalman filter is applied to estimate the position, velocity and acceleration of the vehicle moving from above the atmosphere and through the atmosphere. While the vehicle moves above the atmosphere the Kalman filter calculates estimates of position and velocity. Detecting the moment of atmospheric reentry is obtained with taking into account a residual which is equal to a difference between measurement of range and predicted range. This residual is used to estimate the height of atmospheric reentry.
Incomplete Multi-view Multi-label Active Learning

The label information of training data is crucial for effective machine learning in many domains, while it is expensive to annotate data at a large-scale by domain experts. The problem was intensified by the multiplicity and incompleteness of multiview multi-label (MVML) objects, which is ignored by almost all existing multi-view multi-label active learning approaches. In this paper, we propose an incomplete multi-view multi-label active learning (iMVMAL) approach to reduce the cost of querying MVML data. iMVMAL firstly extends under-complete Autoencoder to learn the shared/individual representations of samples across/within incomplete views by an indicator matrix to indicate the missing samples of respective view. As such, the optimization of the Autoencoder’s parameters will not be impacted by the missing samples. Next, it uses the extracted shared/individual information to train multiple classifiers and to quantify the informativeness of sample-label pairs from these classifiers, from label-wise and sample-wise information also. After that, it selects the sample-label pairs with the highest informativeness for query. Empirical studies on benchmark datasets show that iMVMAL outperforms competitive baselines at the same query cost in the complete multi-view setting, and maintains its effectiveness in the incomplete multi-view setting as well.
Integration of Self Sovereign Identity in Security Systems

Transactions over the internet have increased rapidly and so is the need to prove one’s identity and have a secured system to keep records. To overcome identity theft and fraud cases, Self-Sovereign Identity (SSI) was introduced which gives the user complete control over their identity on the internet. Self-Sovereign Identity eliminates the centralized authority and brings Zero-knowledge proof concepts into account to help in easy transactions over the internet. It avoids revealing unnecessary information and correlation attacks. Self-Sovereign Identity, on top of blockchain public ledger features, provides an extra security layer to the system that can be used to monitor the entries in confidential places. This research paper describes a software module, that we have developed, to grant verifiable credentials to users. These credentials, on verification, can grant entry into any security system with which the module is integrated. The module utilizes the facilities provided by Hyperledger Indy and Hyperledger Aries for the creation of verifiable credentials and subsequent verification in a secure manner.
Two-Stream Completeness Modeling for Weakly Supervised Temporal Action Detection

In the process of dataset construction, the cost is expensive to make frame-wise temporal annotations for videos. Therefore, weakly supervised temporal action detection methods which only leverage video-level action categories annotations during training have become an important research branch. At present, most of the weakly supervised methods adopt feature early fusion, and have two inevitable problems, namely action completeness and background frame interference. Therefore, this paper proposes a method based on the Two-Stream Completeness Modeling (TSCM) network. First, this method separately inputs spatial flow features and temporal flow features into the network to make full use of the characteristics of the two modal features. Second, it employs the modeling of the multibranch complementary completeness to generate as complete action instances as possible. Finally, Angular Center Loss with a Pair of Triplets (ACL-PT) is designed to suppress the interference from background frames. In particular, this paper constructs a temporal action detection dataset (STAD), which is based on learning scenes to explore the effectiveness of our method in real applications. Experimental results show that the proposed TSCM method not only is superior to most mainstream methods in terms of mean Average Precision (mAP) on the THUMOS14 dataset and the ActivityNet1.2 dataset, but also achieves good detection accuracy on the STAD dataset.
Enhancing Adversarial Examples on Deep Q Networks with Previous Information

Reinforcement learning has been widely used in many applications (e.g., self-driving cars, games, and robots). However, it is not very efficient when there are many actions and states. Thus, many researchers have applied Deep learning to it and call it Deep reinforcement learning (DRL) to address such a drawback. Unfortunately, recently, some researchers discovered a weakness of Deep learning in the test time and called it an adversarial attack. Since DRL is also Deep learning, it has the same weakness. Hence, DRL is vulnerable to adversarial attacks. Moreover, some existing works have created adversarial attacks for DRL. Generally, they first decide whether to create an adversarial example for the current state and then determine how much perturbation to add to the current state. This limits the adversary from the information in the previous steps when determining the perturbation. Also, some of the attacks fixed the adversarial action to the worst action, and then, the behavior of the target agent did not look natural. Therefore, we propose combining those two problems into one problem to allow the adversary to receive the information from the previous steps and let the adversary pick an adversarial action that is worth adding the perturbation by formulating and optimizing the problem. At last, we construct the experiment on Atari games to investigate the behavior of the agent attacked by our approach and compare our approach to the state-of-the-art attacks in terms of the amount of added perturbation and reward. As a result, our approach can make an agent play the games as if there was no adversarial attack and outperform the previous works.
Quantum delegated computing ciphertext retrieval scheme

Quantum physics has some unique properties, such as quantum entanglement and quantum superposition. Encryption algorithms designed based on quantum physical properties usually have higher security than classical computing. Applying quantum cryptography to data retrieval can realize cloud database encryption of quantum information. We propose a cloud ciphertext retrieval scheme, which combines quantum encryption and quantum search algorithm to ensure the privacy of data while the server completes information retrieval. In this scheme, the data are stored in the form of qubits string and placed in the quantum superposition state. We establish the mapping relationship between classical information and quantum states. Our scheme solves the problem of a large amount of computing and lack of sufficient computing resources at the client, assigns computing tasks to servers with sufficient resources, and ensures the security of information.
Introduction

Nowadays it is more common for researchers in the area of computer science to work with bio-inspired metaheuristicsMetaheuristics to solve optimizationOptimization problems. There are many of papers and algorithms that researchers have developed based on natural behaviors that have been recently proposed.