Reviewerly/exHarmony huggingface dataset:
The crisis of education in conditions of the covid-19 pandemic: The model of blended learning

Purpose of the study: The paper presents a topical problem of modern higher education in the context of the transition to distance learning for the period of active spread of the coronavirus COVID-19. To date, a decision has been made to partially or completely close schools and universities in 191 countries around the world. As a result, about 1.6 billion students switched to distance education. In this connection, today this area of ​​the system of education is of the highest importance, both for individual countries and for the entire world community. Methodology: The leading approach to the study of the problem of distance learning in the system of higher professional education was a systematic approach as a methodological principle of modern science. The scientific and theoretical teachings about distance learning, the distance learning technology, the model of blended learning in higher education; theoretical propositions on the methods and technologies of providing distance learning in higher education became the fundamental basis of this study.Results: The paper presents the study results of the most relevant trends in education: omni-learning (learning with the use of all possible communication channels), adaptive learning, distance learning, online mentoring, blended learning, self-learning organizations, EdTech startups, etc. The results of a comparative analysis of traditional forms of learning and blended learning are presented. It has been proven that blended learning is the most effective form of distance learning. Application of this study: We can conclude that in the current socio-economic conditions of the development of the system of higher professional education, the introduction of a blended learning model in the framework of the conception of lifelong education seems to be necessary and obvious in our opinion. Proceeding from this conception, specialists of any profession need continuous improvement of knowledge, skills, professional competencies, which is possible in the framework of the blended learning model using e-learning technologies.Novelty / originality of this study: The conclusions have been made that blended learning makes it possible to translate the learning process into the information space; contribute to the formation of the learners competencies stipulated in the Federal State Educational Standard and also creates the conditions for the development of readiness and the students ability to self-actualize.
Comparison of Text-Based and Feature-Based Semantic Similarity Between Android Apps

Textual description of mobile apps comprised of apps’ core functionality and describes the key features of any given app. Finding semantically similar apps help app users searching and looking for alternatives in the app stores as well as help the developers learn from their similar apps. A number of works have been done on apps’ text-based semantic similarity. However, no attempts have been taken yet to find similar apps based on semantic similarity of their features. Moreover, varying length of textual descriptions affect semantic approach significantly. In this paper, we employ two different semantic similarity techniques to both the text-based and feature-based app descriptions. Together we investigate four different techniques and compare the similarities generated between the given android apps. We compare our results with a truth-set built on 50 similar and 50 non-similar apps in the same domain and across the other domains respectively. Experimental results demonstrate that feature-based approach generate better results in determining app similarity in the same domain and across the other domains irrespective of the length of apps’ textual descriptions.
A Framework for Homogeneous Cross-Project Defect Prediction

Often, the prior defect data of the same project is unavailable; researchers thought whether the defect data of the other projects can be used for prediction. This made cross project defect prediction an open research issue. In this approach, the training data often suffers from class imbalance problem. Here, the work is directed on homogeneous cross-project defect prediction. A novel ensemble model that will perform in dual fold is proposed. Firstly, it will handle the class imbalance problem of the dataset. Secondly, it will perform the prediction of the target class. For handling the imbalance problem, the training dataset is divided into data frames. Each data frame will be balanced. An ensemble model using the maximum voting of all random forest classifiers is implemented. The proposed model shows better performance in comparison to the other baseline models. Wilcoxon signed rank test is performed for validation of the proposed model.
Trust-Aware Curation of Linked Open Data Logs

Trust was widely discussed and formalized in the literature. In the context of Big Data and Connected World, it becomes crucial for developing data-driven solutions. Trusted data increase the quality of decision support systems. Recently, companies are racing towards Linked Open Data (LOD) and Knowledge Bases (KB) to improve their added value, but ignore their SPARQL query-logs. If well cured, these logs can present an asset for analysts. A naive and direct use of these logs is too risky because their provenance and quality are highly questionable. Users of these logs in a trusted way have to be assisted by providing them with in-depth knowledge of the whole LOD environment and tools to cure these logs. In this paper, we propose an ontology-based model inspired by the recent developments in $$<trust, risk, value>$$ -ontology engineering. Then, a trust-aware curation approach is presented, composed of enriched ETL-like operators integrating trust metrics that keep only trustworthy queries. Finally, experiments are conducted to study the effectiveness and efficiency of our proposal.
A Collaborative Effort-Benefit-Value Analysis Model to Support Requirements Reuse for Software Requirements Prioritization

This paper proposes a collaborative method to prioritize the set of requirements backing the concept of requirement re usability through crowd sourcing platform. Both stakeholder and developer perspectives are captured and effort-benefit-value analysis model is presented. Stakeholder priority is captured using Fuzzy TOPSIS model, and developer priority is captured by analyzing inter-relationships among requirements in the form of dependency. Further, a provision is provided to resolve conflicts between requirement pairs in an interactive manner to produce final ranking accepted to both with minimum conflict. An experimental proof of concept is provided by comparing results of proposed approach with state of art, fuzzy analytic hierarchy process (FAHP), interactive genetic algorithm (IGA) techniques, and on a dataset. Results of experimentation conclude that presented approach is capable of producing accurate and comparable results in addition to handling issue of dependency, multi- criteria preferences, reliability, and robustness to errors.
Design of Two-Dimensional Recursive Digital Filter Using Multi Particle Swarm Optimization Algorithm

Particle Swarm Optimization (PSO) is an evolutionary algorithm widely used in optimization problems. It is characterized by a fast convergence, which can lead the algorithm to stagnate in local optima. In the present paper, a new Multi-PSO algorithm for the design of two-dimensional infinite impulse response (IIR) filters is built. It is based on the standard PSO and uses a new initialization strategy. This strategy is relayed to two types of swarms: a principal and auxiliaries. To improve the performance of the algorithm, the search space is divided into several areas, which allows a best covering and leading to a better exploration in each zone separately. This solved the problem of fast convergence in standard PSO. The results obtained demonstrate the effectiveness of the Multi-PSO algorithm in the filter coefficients optimization.
Action Change Detection in Video Based on HOG

Background and Objectives: Action recognition, as the processes of labeling an unknown action of a query video, is a challenging problem, due to the event complexity, variations in imaging conditions, and intra- and inter-individual action-variability. A number of solutions proposed to solve action recognition problem. Many of these frameworks suppose that each video sequence includes only one action class. Therefore, we need to break down a video sequence into sub-sequences, each containing only a single action class.Methods: In this paper, we develop an unsupervised action change detection method to detect the time of actions change, without classifying the actions. In this method, a silhouette-based framework will be used for action representation. This representation uses xt patterns. The xt pattern is a selected frame of xty volume. This volume is achieved by rotating the traditional space-time volume and displacing its axes. In xty volume, each frame consists of two axes (x) and time (t), and y value specifies the frame number.Results: To test the performance of the proposed method, we created 105 artificial videos using the Weizmann dataset, as well as time-continuous camera-captured video. The experiments have been conducted on this dataset. The precision of the proposed method was 98.13% and the recall was 100%.Conclusion: The proposed unsupervised approach can detect action changes with a high precision. Therefore, it can be useful in combination with an action recognition method for designing an integrated action recognition system.
Increased Mammogram Image Contrast Using Histogram Equalization And Gaussian In The Classification Of Breast Cancer

Breast cancer is one of the most common diseases among women in several countries. One of the most common methods to diagnose breast cancer is mammography. In this study, we propose a classification study to differentiate benign and malignant breast tumors based on mammogram image. The proposed system includes five major steps, i.e. preprocessing, histogram equalization, convolution, feature extraction, and classification. Image is cropped using region of interest (ROI) at preprocessing stage. In this study, we perform image contrast quality enhancement of the mammogram to view the breast cancer better. Image contrast enhancement uses histogram equalization and Gaussian filter. Gray-Level Co-Occurrence Matrix (GLCM) is used to extract the mammogram features. There are five features used i.e. entropy, correlation, contrast, homogeneity, and variance. The last step is to classify using naïve Bayes classifier (NBC) and k-nearest neighbor (KNN). Based on the hypothesis, the accuracy of NBC method is 90% and the accuracy of KKN method is 87.5%. So, the mammogram image contrast enhancement is well performed.
Adaptive Smoothing for Path Integral Control

In Path Integral control problems a representation of an optimally controlled dynamical system can be formally computed and serve as a guidepost to learn a parametrized policy. The Path Integral Cross-Entropy (PICE) method tries to exploit this, but is hampered by poor sample efficiency. We propose a model-free algorithm called ASPIC (Adaptive Smoothing of Path Integral Control) that applies an inf-convolution to the cost function to speedup convergence of policy optimization. We identify PICE as the infinite smoothing limit of such technique and show that the sample efficiency problems that PICE suffers disappear for finite levels of smoothing. For zero smoothing this method becomes a greedy optimization of the cost, which is the standard approach in current reinforcement learning. We show analytically and empirically that intermediate levels of smoothing are optimal, which renders the new method superior to both PICE and direct cost-optimization.
Deep aligned feature extraction for collaborative-representation-based face classification with group dictionary selection

Face recognition plays an important role in many robotic and human–computer interaction systems. To this end, in recent years, sparse-representation-based classification and its variants have drawn extensive attention in compress sensing and pattern recognition. For image classification, one key to the success of a sparse-representation-based approach is to extract consistent image feature representations for the images of the same subject captured under a wide spectrum of appearance variations, for example, in pose, expression and illumination. These variations can be categorized into two main types: geometric and textural variations. To eliminate the difficulties posed by different appearance variations, the article presents a new collaborative-representation-based face classification approach using deep aligned neural network features. To be more specific, we first apply a facial landmark detection network to an input face image to obtain its fine-grained geometric information in the form of a set of 2D facial landmarks. These facial landmarks are then used to perform 2D geometric alignment across different face images. Second, we apply a deep neural network for facial image feature extraction due to the robustness of deep image features to a variety of appearance variations. We use the term deep aligned features for this two-step feature extraction approach. Last, a new collaborative-representation-based classification method is used to perform face classification. Specifically, we propose a group dictionary selection method for representation-based face classification to further boost the performance and reduce the uncertainty in decision-making. Experimental results obtained on several facial landmark detection and face classification data sets validate the effectiveness of the proposed method.
RETRACTED: Exploration of Multi-modal English and American Literature Teaching Based on Computer Network

Abstract At present, to teach British and American literature is still in the traditional bondage, and has not followed the rapid progress of social economy and culture, so it has come to various problems and difficulties. Based on the development of computer network technology and various forms of network derivative technology, this paper puts forward the concept of multi-modal teaching, and briefly describes its feasibility, mainly through the practical application of multi-modal computer network and theoretical exploration to explain the problem.
Single Image Dehazing by Predicting Atmospheric Scattering Parameters

In this work we propose a method for single image dehazing that exploits a physical model to recover the haze-free image by estimating the atmospheric scattering parameters. Cycle consistency is used to further improve the reconstruction quality of local structures and objects in the scene as well. Experimental results on four real and synthetic hazy image datasets show the effectiveness of the proposed method in terms of two commonly used full-reference image quality metrics.
Cybersecurity and Cybercrime Combatting Culture for African Police Services

Police forces are responsible to investigate cybercrimes and to protect their own assets from cybersecurity attacks. The majority of police forces find it difficult to fulfil their responsibilities in this regard in the face of constrained funding, a lack of awareness and training amongst law enforcement staff, the growing number of cybercrime incidences, and outdated or insufficient technology and infrastructure. Even if police forces are able to install technical controls to counter cyber threats, their staff members’ cyber behaviour may be a weak link in the cybersecurity chain and will probably not have sufficient training. The cultivation of a cybersecurity culture has been shown to be the best approach to address human behaviour in the cyber domain. There are several frameworks and other resources available for an organisation to cultivate a cybersecurity culture but the organisational culture in law enforcement agencies is different than that in other organisations. The cyber behaviour, cybercrime investigation skills, training and education of police force members require customised strategies and research. African police forces find it particularly difficult to deal with these challenges due to a lack of funding and a shortage of cybersecurity capability and capacity. This paper presents guidelines for African police forces to formulate strategies and plans to train and educate their members and to foster an organisational cybersecurity and cybercrime combatting culture.
Context-Aware Automatic Text Simplification of Health Materials in Low-Resource Domains

Healthcare systems have increased patients' exposure to their own health materials to enhance patients' health levels, but this has been impeded by patients' lack of understanding of their health material.We address potential barriers to their comprehension by developing a context-aware text simplification system for health material.Given the scarcity of annotated parallel corpora in healthcare domains, we design our system to be independent of a parallel corpus, complementing the availability of data-driven neural methods when such corpora are available.Our system compensates for the lack of direct supervision using a biomedical lexical database: Unified Medical Language System (UMLS).Compared to a competitive prior approach that uses a tool for identifying biomedical concepts and a consumer-directed vocabulary list, we empirically show the enhanced accuracy of our system due to improved handling of ambiguous terms.We also show the enhanced accuracy of our system over directly-supervised neural methods in this low-resource setting.Finally, we show the direct impact of our system on laypeople's comprehension of health material via a human subjects' study (n = 160).
Archive Fund of the Bibliographer M.Z. Vinokouroff in the Alaska State Library (USA)

This paper presents the study of the information potential of documents from the personal fund of Michael Z. Vinokouroff, stored in the Alaska Historical Library (USA). Their source analysis will determine the directions of their further scientific use. Numerous documents, diaries, letters, books, photographs of the archive fund are the objects of special attention of researchers of the Russian Orthodox Church, “Russian America”, historians, bibliographers, linguists, literary scholars. The main directions of the effective scientific use of the documents from the personal archive of Michael Z. Vinokouroff. In addition to the unpublished diaries and notes of the bibliophile, the collection contains letters from St. Innokenty (Veniaminov), as well as documents reflecting the political and literary life of Yakutia in the first third of the 20th century.
Integrating Digital Technology into Elementary Mathematics: Three Theoretical Perspectives

In this article, the author’s intent is to begin a conversation centered on the question: How was the integration of digital technology into elementary mathematics classrooms framed? In the first part of the discussion, the author provides a historical perspective of the development of theoretical perspectives of the integration of digital technology in learning mathematics. Then, the author describes three theoretical perspectives of the role of digital technology in mathematics education: microworlds, instrumental genesis, and semiotic mediation. Last, based on three different theoretical perspectives, the author concludes the article by asking the reader to think differently.
Long-term Object Tracking Based on Improved Continuously Adaptive Mean Shift Algorithm

Long-term object tracking encounters complex scene changes, such as deformation, short-term departure from sight, occlusion, and lighting changes, resulting in complex and unstable tracking. To improve the accuracy and success rate of long-term object tracking in complex scenes, an improved continuously adaptive mean shift (CAMShift) algorithm was proposed. The joint probability density distribution of the target model was obtained by using the Bhattacharrya coefficient to calculate the contribution of the color features and texture features. Combining with the fused target model and Kalman filter, the target position was obtained by implementing CAMShift algorithm. Finally, a template pool was designed to store high-confidence tracking results. The target template was updated online by retrieving the initial frame from the template pool to recover re-detection after tracking drift or failure. The accuracy of the proposed algorithm was verified by simulation analysis. Results show that the distance precision and success rate of the proposed algorithm are 0.9 and 0.83, respectively. The proposed algorithm effectively solves long-term target tracking problems affected by complex scenes, such as occlusion, similar colors, and deformation. This study provides references for the automatic detection of traffic incidents in the intelligent traffic monitoring system.
Latent Graph Predictor Factorization Machine (LGPFM) for modeling feature interactions weight

Regression is a machine learning model that predicts the target based on input data. Factorization Machines (FMs) are new class models that in addition to regression, present factorized interactions between a pair of features. FMs have been proven to accomplish good accuracy for the rating prediction tasks such as recommender systems. However, FM models all the interactions with the same weight which can be ineffective, since useless interactions may cause noisy results. In this paper, we propose a new model named: Latent Graph Predictor Factorization Machine (LGPFM) that capture the interaction weight of each pair of features using Convolutional Neural Network (CNN). LGPFM combines FM model with the benefits of the CNN that works efficiently in grid-type topology, which would improve significantly the accuracy of results.
Chinese Shallow Semantic Parsing Based on Multi-method of Machine Learning

With the rapid development of 5G+ information intelligence, higher requirements are put forward for accurate and efficient semantic annotation methods. Semantic role annotation for any single method at present has its obvious and complementary advantages and disadvantages. Therefore, this paper attempts to introduce the above three mainstream and stable annotation methods into each task of semantic role annotation, and designs a Chinese semantic role annotation that integrates multi-method. This method integrates the statistical-based linear sequence method, the rule-based hierarchical tree method and the most advanced deep learning in the four processing modules of semantic role annotation. Multi-level linguistic features are introduced into the feature arrangement of the model to realize the mutual combination of multiple modules. Experiments show that the modular fusion of steps and methods effectively improves the annotation performance of each step of annotation.
Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds.

This paper introduces novel results for the score function gradient estimator of the importance weighted variational bound (IWAE). We prove that in the limit of large $K$ (number of importance samples) one can choose the control variate such that the Signal-to-Noise ratio (SNR) of the estimator grows as $\sqrt{K}$. This is in contrast to the standard pathwise gradient estimator where the SNR decreases as $1/\sqrt{K}$. Based on our theoretical findings we develop a novel control variate that extends on VIMCO. Empirically, for the training of both continuous and discrete generative models, the proposed method yields superior variance reduction, resulting in an SNR for IWAE that increases with $K$ without relying on the reparameterization trick. The novel estimator is competitive with state-of-the-art reparameterization-free gradient estimators such as Reweighted Wake-Sleep (RWS) and the thermodynamic variational objective (TVO) when training generative models.