Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Management of Strategic Grain Reserves

Food is a daily requirement for everyone, while production patterns are seasonal. With the rise in world population and recent climate changes, it is becoming difficult to ensure that households can access affordable, nutritious food. Producing sufficient nutrients is becoming more difficult because water and soil resources are already stressed and will be further strained by climate change. Improving food security has several domains: complex climate models, genetics research, modeling market and household behaviors, political shocks, and detailed environmental research. Moreover, big data analytics is a great tool for modeling and understanding relations between the market, food access, household income, local food availability, and predicting future requirements in developed countries. These advantages have lagged in developing countries, and data analytics can not be applied due to the gaps in data availability and modeling. This work currently focuses on improving food security by managing the available production through strategic grain reserves. The governments store grains to improve national food security. The increasing demands for more transparency in public administration increase new technology to increment the management capacity, focusing on better controls and governance mechanisms. Blockchain technology emerges as a promising technology to enhance visibility, transparency, and data integrity with an immutable distributed ledger to increase trust in the parties' business transactions. This paper discusses blockchain technology and its potential role in strategic grain reserve management for better control, monitoring, and producing real-time reliable tamper-proof data to develop critical models for big data analytics and future prediction systems.
Psychosocial Aspects of Cybercrime Victimization in Pakistan

This research assess and analyzes cybercrime's current state concerning the encompassing psychosocial context of victimization in Pakistan. This research discusses, explains, and interprets the determinants behind the psychosocial aspects of cybercrime victimization and combines the factors incorporated with the causes, patterns, and nature of psychosocial dilemmas in the Pakistani context. Qualitative research methodology by using purposive sampling is adopted, and five case studies are taken. This research is explanatory in its very nature. Unfortunately, Pakistan possesses an ideal environment for the growth of cybercrime. Women register 70% of the cybercrime cases, and more than 60% of crimes are Facebook related. Government officials were involved in leaking the private information of the victims. Most cases are filed against family members, and such cases get dropped even before a trial, as male members of the family represent females. Patriarchy reigns in its real sense as women comparatively have nominal opportunities in Pakistan.
Improvement of CIFAR-10 Image Classification Based on Modified ResNet-34

Image classification is one of the fundamental tasks in computer vision. In this paper, a series of ablation experiments were implemented based on ResNet-34 architecture, which integrates residual blocks with normal convolutional neural network and contains 34 parameter layers, to improve CIFAR-10 image classification accuracy. Swish activation function, fine-tuning model architecture, regularizing model and adjusting learning rate were employed to improve the performance of ResNet-34. The results of the experiments show that the accuracy of ResNet-34 increased from initial 78.23% to 94.17%, which indicates that the modified ResNet-34 has a quite great progress in natural objects recognition. The results of this paper can be used to other models to gain greater performance enhancement.
A Genetic Algorithm as a Classifier

The significant problem on the machine learning is a pattern recognition. The recognition based on classification methods. There are many classification methods that use adaptive learning AI. The Genetic Algorithms are prominent for optimization but not good in classification. The authors try to find out and present new improvement methods based on the simple Genetic Algorithm and the classic XOR problem. We found how to make a Genetic Algorithm solve the classic problem. The break-through results encourage the researchers to reconsider Genetic Algorithm as a classifier.
Language and Cultural Identity of Phraseological Units with Proper Names in the Italian Publicistic Discourse

The article aims to reveal language and cultural identity of phraseological units with proper names in the Italian publicistic discourse. Precedence of proper names in the structure of phraseological units is considered as a basis of their language and cultural identity. The paper has interdisciplinary nature; it is written at the junction of linguo-culturology and phraseology. Scientific originality of the study lies in the fact that the author examines a narrow group of phraseological units - phraseological units with proper names. The research findings are as follows: the author classifies proper names that determine language and cultural specificity of publicistic texts, identifies components of language and cultural identity of the phraseological units under study.
Periodogram and Other Direct Spectral Estimators

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AntiPhishTuner: Multi-level Approaches Focusing on Optimization by Parameters Tuning in Phishing URLs Detection

Phishing is an alarming issue among the cybercriminals. In the last decade, online services have revolutionized the world. Due to the revolutionary transformations of web service, the reliance on the web has increased day by day. Security threats have emerged due to the increasing reliance on online orientation. There are many types of anti-phishing solutions available that have been proposed by many researchers. However, this chapter is to propose an intelligent framework to detect phishing URLs based on the optimized learning architecture scheme. Multi-layer based structures have been implemented to detect phishing URLs using Deep Neural Network (DNN), Neural Network (NN) and Stacking. These architectures are evaluated with various tuning hyper-parameters to obtain the optimized output named AntiPhishTuner. As a result, five-layer based DNN can provide accuracy of 0.95 with the minimum mean squared error (MSE) 0.30, and also a mean absolute error (MAE) 0.074 where the number of epochs was 50 and Adam optimizer as an optimizer. Using two-layer NN with AdaGard optimizer can provide accuracy of 0.95, with MSE 0.30 and MAE 0.074. NN provides these results with 150 epochs. Stack generalization can reach maximum accuracy 0.97 in binary classification with MAE 2.1. This chapter can provide a better lead to researchers and anti-phishing tools developers to make an initial decision about the approach that should be followed for further extension.
A suggested taxonomy for Governmental Clouds

Cloud Computing has undoubtedly been the most disruptive IT paradigm of the last two decades, spanning a wide area of technologies and implementations. Its emergence is already transforming the public sector with relevant e-Government use cases. The adoption of various cloud-related initiatives as part of national strategies is driving the need to use a common framework to better describe, analyse and compare those implementations, known as "Governmental Clouds". In addition, for any interoperability to be effective in the long run, a standard framework and common language to describe the relevant ontologies should exist. This paper aims to introduce and discuss a taxonomy of three different models based on the existing and emerging Cloud Computing governmental best practices and available use cases, addressing the lack of relevant concrete definitions.
Efficient Techniques for Detecting Malignant Tumor in Breast at an Early Stage: A Conceptual and Technological Review

Breast cancer is one of the most common forms of cancer occurring among the female population. The predominant medical examination used for breast cancer screening is mammography. Due to the malignant nature of breast cancer, the accuracy of the examining procedures becomes crucial especially when it is interlinked with the survival of human life. Mammography results are highly influenced by the false negatives cases due to the lack of an automated system that can screen the images correctly with accuracy. A false negative is considered an erroneous diagnosis by a doctor. The treatment of breast cancer is difficult as certain tumors have susceptible visibility, and it is visible only after the tumor has developed and has an increase in the size. Medical image processing is an important part of cancer detection that can provide useful clinical information about the structure, morphology, and metabolism for a successful investigation and treatment. Automated image processing techniques, deep learning techniques, and neural network techniques can aid in different stages related to cancer staging, the prognosis of the disease, and the suggestion of the appropriate treatment and therapy. The main drive of the research corresponds to finding the most efficient image processing techniques, deep learning techniques, and neural network techniques that can produce more accurate results, thus being able to aid the doctor to efficiently detect cancer at an early stage and assist in making successful clinical decisions.
Dimension of a Subset of Residue Classes

This paper introduces a problem in additive number theory which is motivated by optimization of hardware implementation of QC-LDPC codes. For a fixed subset S, S ⊂ℤ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> , of residue classes modulo q the object of interest is a basis set G, G ⊂ℤ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> , of minimal size, such that every element of S is representable as a sum of several elements from G. For a fixed number k, k ≤⌈log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> q⌉, the object of interest is a function ζ(q,k) defined as a maximum number such that, for every set of cardinality <; ζ(q,k), there exists a basis set of cardinality <; k.
The personality of a teacher in contemporary socio-cultural space

The article deals with issues related to the formation of the personality of a future teacher. The main goal of modern education system is to form an intellectual, moral and harmonious personality. Therefore, the teacher is entrusted with the mission to be a translator and navigator in the information space. development of a future teacher’s personality in the modern socio-cultural space is conditioned by motivational settings to achieve positive results of own activity, conditions of self-realization in the process of university training, participation of teacher trainees in national projects and contests of teaching skills, providing modern infrastructure of higher school teachers’ training system.
Citation Analysis of Journal-to-Journal Data : Deriving Clusters for Journal Classification

This bibliometric study is a citation analysis of journals that concern themselves with businessrelated topics. For this purpose, the journals from the following five categories from the Web of Science Subject Categories have been examined: “Business”, “Business & Finance”, “Economics”, “Management” and “Operations Research & Management Science”. The data is retrieved from the Journal Citation Reports 2019 for each journal that is part of one of the categories. The data includes information about the journals that the articles published in the specific journal in 2019 cited and which journals cited the articles in a specific journal (Cited Journal Data and Citing Journal Data). This data is combined by creating an asymmetrical 1- mode matrix of all journals. Then, the matrix is analyzed with Pajek and VOSviewer to create clusters of journals with a high inter-correlation through citations. In the following step, the journals in these clusters are analyzed for their disciplines, main topics, and compared to typical fields of study at universities (e.g. Accounting, Marketing, Finance, etc.) in order to highlight similarities and differences between the fields of studies and the clusters of journals.
An Activity Recognition Framework for Overlapping Activities using Transfer Learning

Activity recognition is gaining popularity with the increase in digital content. In video data, there is a lot of information hidden that needs to be explored. Human Activity Recognition (HAR) in video streams applies to many areas, such as video surveillance, patient health monitoring, and behavior analytics. Variation in the environment, view-point changes, occlusion, and illumination are some main challenges in HAR. Among other challenges, there is also a similar activity or overlapping activity issue that has not been explored much in past. Resolving overlapping activities classes issues can be a major contribution to overall Human Activity Recognition. Hand-crafted methods and traditional Machine Learning methods were extensively explored in past. Recently, many Deep Learning-based methods are achieved high accuracy. Convolutional Neural Network (CNN) and 3D CNN methods outperform other methods. In this paper, we proposed a Transfer Learning-based Human Activity Recognition (TLHAR) for video data streams. We used VGG16 and InceptionV3, two pre-trained CNN models, and utilized their prior training knowledge for efficient activity recognition. The purposed system outperformed existing activity recognition methods and showed state-of-the-art accuracy and less computational cost requirements than other techniques by taking the benefits of Transfer Learning.
RETRACTED: Design of life assistance device for the elderly based on artificial intelligence

Abstract With the development of the times, China has entered a stage of aging population. Because of the increase of age, the elderly are often hard to finish like bend over, squat such simple actions, which brought great challenge to the basic life of old people. Some basic activities such as walking and the toilet, are very difficult for them. In order to guarantee the basic life of the elderly, a kind of life assistance device for the elderly based on artificial intelligence is designed to help the elderly in walking and toilet. The mechanical structure of the device mainly consists of five parts, namely, walking mechanism, lifting mechanism, rotating seat cushion mechanism, rotating handrail mechanism and pulling rod backrest mechanism. The control part is mainly based on voice recognition technology. The device is simple in structure, convenient in operation and low in cost. Compared with the existing life assistance devices for the elderly, the device has more complete functions, which better solves the difficulties of the elderly in walking and using the toilet, and provides great convenience for the elderly.
Reliability of Heuristic Evaluation During Usability Analysis

Scientific reliability—the degree to which a research method produces stable and consistent results—represents a major linchpin of tenable scientific research. A number of different studies support the conclusion that usability testing of software interfaces lacks scientific reliability (Jacobsen et al., 1998; Jordan, 2017). The present report augments this conclusion with findings that heuristic evaluation, the second major pillar of usability analysis, also lacks reliability. We conclude that a key priority of “usability science” should be to initiate a systematic program of inquiry to investigate the degree to which this term has meaning.
Data augmentation for ancient characters via Blend-Font Net

Historical documents record a lot of precious information through ancient characters. However, some problems like unbalanced character samples and intra-class multi-modality inside the documents are critical factors that limit the performance of existing character recognition technologies. Therefore, we propose a two-stage font generation model, Blend-Font Net, which use some easy to get modern character datasets to augment ancient character dataset and solve these mentioned problems based on blend-font strategy. The model generates new samples by extracting and modifying the font information from the character image. A font generation model learns the mapping between different fonts in the first stage, and the slightly modified model learns how to generate samples that blend two different fonts in the second stage. Extra samples are generated for balancing historical documents dataset through the proposed model. Experiments show that our results have diverse visual effects and improve the accuracy of the text recognition network. Furthermore, the proposed method shows a broad application prospect in similar works as no font label required and multi-modality problem solved.
Implementation and use of cloud technologies in the educational process

The necessity of making changes in the educational process (EP) through the use of cloud, network technologies is considered. This training format allows you to apply new forms of training, communicate with teachers and students outside the classroom.An experiment has been carried out showing the advantages of cloud technologies in organizing lifelong learning of students. For the study, two groups of 1st year students were taken. One group learned through a conservative, classroom EP, the other through the cloud. The students of the second group turned out to be more interested in learning, according of the final testing, they showed a high quality of residual knowledge.The results of the study showed that the addition of cloud technologies to the educational process increases the student’s cognitive interest and allows him to be always and everywhere immersed in the EP. The modern student lives in the world of information technology and it is necessary for him to create a familiar atmosphere in education, to use the tools familiar to him. Learning should be more interactive. Then the student after graduation will become a real, knowledgeable specialist who will be able to apply the knowledge gained in his work. The disadvantages of cloud technologies have also been identified. The level of computer literacy of the majority of teachers will not allow them to use new solutions everywhere.
A Double Stream Module in Backbone for Object Detection Network

The representation ability of the network is crucial for object detection. In this paper, we present a novel building block named double stream block to construct a backbone called double stream module (DSM) to improve the representation ability of object detection networks. The double stream block is designed by fusing the shuffle branch and the non shuffle branch with a weight layer. To make our block adaptive for the depth of the convolutional layer, a shortcut connection is adopted after the double stream fusion. After that, DSM is built as a backbone and integrated into Faster-RCNN, Cascade-RCNN, DetectoRS detection system to verify its applicability. On the VisDrone dataset of VAV, the evaluation results for object detection show that the double stream design can significantly increase the representation ability of the network, and our approach gains 2.0, 0.5and 1.1 mAP improvements in the detection systems mentioned above respectively.
Single Image De-raining via Densely Connected Pyramid Network

Images captured in rainy outdoor scenes often suffer from poor visual quality due to the appearance of rain streaks in the image. These degraded images drastically affect the performance of many practical vision systems. Hence, image de-raining is urgently required to be addressed. In this paper, we propose a new deep network for single image deraining, called Densely Connected Pyramid Network (DCP-Net). As the dense connection can maximize the information flow along features from different layers and the multi-scale strategy has been successfully applied in many computer vision tasks, we combine these two benefits to design a Densely Connected Pyramid Block (DCPB) as the basic de-raining unit, called DCPB-Unit and introduce a dense connection architecture to connect several DCPB-Units, which strengthens the feature propagation between the DCPB-Units and improves the effectiveness of learning. The proposed network efficiently makes use of the features from multiple layers and learns the rain streaks with different scales and shapes. Specifically, benefitting from computing the rain-free image by subtraction operation in the feature domain rather than in the image domain, the network is able to generate the high-quality rain-free image. Both quantitative and qualitative experimental results demonstrate the proposed method performs favorably against the state-of-the-art de-raining methods.
CONTINUITY IN THE CONTENT OF TEACHING PROJECT AND RESEARCH ACTIVITIES OF SCHOOLCHILDREN, BACHELOR'S AND MASTER'S STUDENTS ON THE EXAMPLE OF THE DISCIPLINE “FOREIGN LANGUAGE”

The article is devoted to the consideration of the process of the successive formation of metasubject results in the framework of project / research activities of students in the context of learning foreign languages. In this regard, the requirements for the degree of formation of metasubject skills formed within the subject of "Foreign language" at various levels of education and taking into account the stages of project activity (including research project) – goal setting, organization and planning, implementation, reflection, presentation. The article describes a project conducted by the Department of foreign languages and linguodidactics of Irkutsk state University called "the Future in the project", implemented in the framework of the subjects "Methodology of teaching foreign languages" at the University and "Foreign language" at school in order to implement the continuity of the content of teaching project/research activities of students at different levels of education.
Dynamical Deep Generative Latent Modeling of 3D Skeletal Motion

In this paper, we propose a Bayesian switching dynamical model for segmentation of 3D pose data over time that uncovers interpretable patterns in the data and is generative. Our model decomposes highly correlated skeleton data into a set of few spatial basis of switching temporal processes in a low-dimensional latent framework. We parameterize these temporal processes with regard to a switching deep vector autoregressive prior in order to accommodate both multimodal and higher-order nonlinear inter-dependencies. This results in a dynamical deep generative latent model that parses the meaningful intrinsic states in the dynamics of 3D pose data using approximate variational inference, and enables a realistic low-level dynamical generation and segmentation of complex skeleton movements. Our experiments on four biological motion data containing bat flight, salsa dance, walking, and golf datasets substantiate superior performance of our model in comparison with the state-of-the-art methods.