Reviewerly/exHarmony huggingface dataset:
File Security Using Hybrid Cryptography and Face Recognition

Currently, data security has been a major issue as data is usually transferred over the Internet between users. Some security flaw might leak sensitive data. For resolving this, we can use cryptography to secure data so that other unauthorized individuals can’t access the data stored in the file. Use of one cryptography algorithm might not provide higher level security for sensitive data. In this paper, we have proposed a new security system which uses three separate cryptography algorithms to provide better security for the data and facial recognition to verify the user who is currently using the system to provide better security. The file to be secured is split into three parts and is encrypted using AES, Blowfish and Twofish, respectively. File and user information are added to the database. The user of the system is verified using facial recognition during the authorization process in decryption phase.
A Preliminary Study on the Application of Computer Technology in Physical Education

The application of modern information technology also promotes the development of school education, and how to better apply modern information technology in teaching has become a key project of modern research. This paper studies and integrates the practical application of information technology in the teaching process, finds out the problems in the application process of information technology, and constructs a new teaching model which combines computer technology and is suitable for physical education teaching, that named “Leader-independent physical education teaching model”. In order to verify the feasibility of this teaching method, this paper carries out a comparative experiment on students’ learning process of tennis knowledge. The experiment proves that this computer technology-based education model can enable students to master sports knowledge more quickly. The model can improve the way of physical education and promote the popularization and application of information technology in physical education.
Students’ Assessment of the Learning Conditions During the Period of Restrictive Measures Related to Preventing the Spread of the New Coronavirus Infection COVID-19

The article presents the key results of assessing the satisfaction of students of higher educational institutions with the conditions of study during the period of restrictive measures related to preventing the spread of the new coronavirus infection COVID-19. The survey of students was carried out by order of the Ministry of Science and Higher Education of the Russian Federation as part of a study on an independent assessment of the quality of conditions for the implementation of educational activities by organizations carrying out educational activities in basic general education programs, educational programs of secondary vocational education, basic vocational training programs, additional general education programs.
Video Event Recognition using Two-Stream Convolutional Neural Networks

Event Recognition aims to classify activities done by a group of people and/or objects in a video into different classes. Convolutional Neural Networks (CNNs) have shown promising results in computer vision tasks, specifically human activity recognition. This paper presents a Two-Stream Convolutional Neural Network for video Event Recognition which uses Xception architecture as its backbone. The dual streams of this method are responsible for extracting spatial and temporal features. The first stream extracts spatial features from RGB frames and the second stream extracts motion features from optical flow images. Due to optical flow estimation complexity, another method for motion estimation between two frames called Thresholding Frame Subtraction (TFS) is used which can speed up the preprocessing phase. The method showed recognition accuracies of 85.6% and 73.2% on VIRAT ground dataset in the best case using optical flow and TFS images respectively which outperform other works on this dataset.
Benchmarking of Fully Connected and Convolutional Neural Networks on Face Recognition: Case of Makeup and Occlusions

We study the feasibility of Fully connected Neural Networks (FNN) fed by principal components and Convolutional Neural Networks (CNN) on a face recognition benchmark in the case of heavy disguise measures. We observed that in certain cases, the local features makes the CNN performance biased. Having introduced an ensemble of neural networks, we observed that global features fed in FNN create different bias. The CNN and FNN bias differentiation can be used for covering the existing gaps in the disguised face recognition.
BCH 2-Bit and 3-Bit Error Correction with Fast Multi-Bit Error Detection

In this paper an new approach combining 2-bit and 3-bit BCH error correction with fast and simple error detection for errors of higher order is presented. Under the assumption that a 2-bit error or 3-bit error occurred, the corresponding correction bits are determined using the syndrome components $$s_1, s_3$$ for 2-bit errors and $$s_1, s_3, s_5$$ for 3-bit errors. These correction bits are used to calculate higher syndrome components up to $$s_{2T-1}$$ , which are compared to the actual corresponding syndrome components. If the syndrome components match, a 2-bit error or 3-bit error was detected. In the case of a syndrome mismatch, an error other than a 2-bit error or 3-bit error occurred and was detected. The proposed method provides a simple way to differentiate between 2-bit errors (3-bit errors) and errors of higher order.
Unmask inflated product reviews through Machine Learning

Product reviews play a crucial role in online customer purchase decisions. In today’s marketplaces, such as Amazon, the quantity and interest of product reviews is growing, along with fierce competition between sellers. At the same time, the phenomenon of fake reviews is ever-growing. Many proposals capable of detecting fake reviews based on Machine Learning (ML) exist in literature. Nevertheless, bad practices implemented by the sellers make genuine reviews difficult to recognize. For instance, some Telegram Channels are known for giving products for free in exchange for 5-star reviews. This work focuses on the analysis of two review data streams of Amazon products. The first one is composed of the reviews corresponding to the products in the AmazonBasics category. The latter collects reviews of the products in the Telegram channels mentioned above. The analysis reveals a substantial dissimilarity between the two sources of reviews, that conducts to the construction of a ground truth dataset employed in the classification model training. The classification activity can assist during product rating interpretation, which could be invalidated by too many fake reviews. The experimental results reveal that 1&2-star reviews are good predictors of the review’s trustworthiness and the product itself.
The Current Situation of Vocal Music Course’ Assessment in Hubei Polytechnic University

The purpose of this research is to study the current situation of vocal music course’s assessment in music education major at Hubei Polytechnic University, China: To improve the vocal music course assessment scientifically, the current vocal music course’s assessment used regular method of evaluation by evaluated the result of learning after lesson by the teacher. After using scientifically variety of assessment such as student’s self-assessment, assessment by teachers during learning process as well as final test, the students had higher ability of singing, the teacher could lay foundation for adjustment and establishment of the assessment system for the class.
Entering Watch Dogs: Evaluating Privacy Risks Against Large-Scale Facial Search and Data Collection

Discovering friends on online platforms have become relatively easier with the introduction of contact discovery and ability to search using phone numbers. Such features conveniently connect users by acting as unique tokens across platforms, as opposed to other attributes, such as user names. Using this feature, in this work, one of our contributions is to explore how an attacker can easily create a massive dataset of individuals residing in a given region (e.g., country) that includes high amount of personal information about such individuals. To identify the active social network accounts of individuals in a given region, we show that brute force phone number verification is possible in popular online services, such as WhatsApp, Facebook Messenger, and Twitter. We also go beyond and show the feasibility of collecting several data points on discovered accounts, including multiple facial data belonging to each account owner along with 23 other attributes. Then, as our main contribution, we quantify the privacy risk for an attacker linking a total stranger (e.g., someone it randomly comes across in public) to one of the collected records via facial features. Our results show that accurate facial search is possible in the constructed dataset and that an attacker can link a randomly taken photo (i.e., a single facial photo) of an individual to their profile with 67% accuracy. This means that an attacker can, on a large scale, create a search engine that is capable of identifying individuals' records efficiently and accurately from just a single facial photo.
End-to-End Scene Text Recognition Network with Adaptable Text Rectification

Scene text recognition has attracted wide attention of academic, since its irregular shape makes text recognition difficult. Because of the influence of angle, shape and lighting, processing perspective text and curved text still faces various problems. This paper presents an end-to-end scene text recognition network (SRATR). SRATR consists of a rectification network based on spatial transform network and an attention-based sequence recognition network. The rectification network is responsible for rectifying the irregular text, which plays a significant role in text recognition network. In addition, the training needs only scene text images and word-level annotations. The recognition network uses the encoder-decoder mechanism to extract feature sequence from the rectified text. Then we translate the feature sequence into a character sequence to output. In the decoder part, we proposed a fractional pickup method, which can eliminate the interference of noise from the text, make the decoder generate a correct region of focus and improve accuracy of text recognition. This is an end-to-end recognition network. Experiments over several of public datasets prove that SRATR has an outstanding performance in recognizing irregular text.
Practical Use of Edutainment Systems for Science Museums with XR Technology

In recent years, various services using XR technology have been developing within commercial and public institutions, including the entertainment industry, to use technology to provide children with enjoyable experiences. Science museums, however, cannot rely simply on entertainment value, as they need to combine entertainment with content that contains educational themes. Educational XR systems generally involve the use of a smartphone or tablet terminal used to capture footage of markers or objects, after which moving images and/or 3D-models are shown on the terminal display. Through observing and interacting with this content, children can deepen their understanding of a given theme or idea. While such systems provide new and exciting opportunities for the activities that can be offered to children, implementing and operating them requires significant costs for science museums. In addition, as for the types of experiences that the XR system can provide, these can be difficult to understand without concrete experience, which can tend to lead into more passive use of the system such as simply observing content. In this paper, in order to offer solutions to these problems, we introduce XR+Crafting content that combines XR technology with creative activities. Through its use of XR technology, the XR+Crafting system proposed in this paper can not only reduce implementation and operation costs, but can also shift the relationship between children and the technology into one that is more active. This paper will also introduce an example of an interactive XR system that incorporates creative activities for children that can be used as content by science museums using XR technology. Building on these suggestions for practical use, we will provide guidelines for the implementation, design, and operation of XR technology at science museums using the XR+Crafting system.
Identification of Gender from Facial Features

Increasing population and changing lifestyle become a more confusing task for detecting gender from facial images. To solve such a fragile problem several handy approaches are readily available in computer vision. Although, very few of these approaches achieve good accuracy. The features like lightning, illumination, noise, ethnicity, and various facial expression hamper the correctness of the images. Keeping these things in mind, we propose our research work on the identification of gender from facial features. The major component of face recognition is to develop a machine learning model which will classify the images this can be done by haar-cascade-classifier. To train the model with images more accurately we would perform few image processing concepts for the data to perform data analysis and preprocessing for structuring our data. This can be done by OpenCV. After that, we have used PCA ( Principle Comprehend Analysis ) to compute Eigenvalues and for the optimal components, we will get the class name from the knowledge base and confidence score from the SVM-based face recognition model. In our project work, we get good accuracy.
Kernelized Heterogeneity-Aware Cross-View Face Recognition

Cross-view or heterogeneous face matching involves comparing two different views of the face modality such as two different spectrums or resolutions. In this research, we present two heterogeneity-aware subspace techniques, heterogeneous discriminant analysis (HDA) and its kernel version (KHDA) that encode heterogeneity in the objective function and yield a suitable projection space for improved performance. They can be applied on any feature to make it heterogeneity invariant. We next propose a face recognition framework that uses existing facial features along with HDA/KHDA for matching. The effectiveness of HDA and KHDA is demonstrated using both handcrafted and learned representations on three challenging heterogeneous cross-view face recognition scenarios: (i) visible to near-infrared matching, (ii) cross-resolution matching, and (iii) digital photo to composite sketch matching. It is observed that, consistently in all the case studies, HDA and KHDA help to reduce the heterogeneity variance, clearly evidenced in the improved results. Comparison with recent heterogeneous matching algorithms shows that HDA- and KHDA-based matching yields state-of-the-art or comparable results on all three case studies. The proposed algorithms yield the best rank-1 accuracy of 99.4% on the CASIA NIR-VIS 2.0 database, up to 100% on the CMU Multi-PIE for different resolutions, and 95.2% rank-10 accuracies on the e-PRIP database for digital to composite sketch matching.
Methodology and methods to assess library innovations efficiency: An approach proposal

The innovative management today is to promote and support organizational strategic goals through the rational use of material, labor and financial resources. The main goal of innovative management in libraries is to build the management system which enables focused search of options, development and implementation of novelties to increase the library’s competitiveness and sustainability and, in its turn, the sustainability of the environment. The specific features of innovations in the library and information sphere are as follows: comparatively rare radical while frequent modernizing innovations; consistency and systemacity (change in any element inevitably changes the library system overall); irreversibility. To assess the value of library innovations, the author suggests to apply the following criteria (characteristics) correlated with the world experience: innovative relevance, financial effectiveness, cultural efficiency, social significance, global sustainability. Each criterion has its own weight while the expertise has to be accomplished within the suggested procedure. This methodology enables to assess the library knowledgeably. It was tested thrice by expert teams – participants in the All-Russia Contest of Library Innovations initiated by the Russian State Library in 2013, 2015, and 2019.
Evaluation of UAS Swarming in a BVLOS Environment

Unmanned Traffic Management (UTM) systems of the future will require networks of systems both on the ground and onboard the Unmanned Aircraft System (UAS) to ensure safe aircraft separation and routing. To achieve that goal, Oklahoma State University has developed a path planning algorithm running onboard UAS using a secondary flight computer to determine paths around obstacles and other vehicles, as well as partnered with GE and the Choctaw Nation to test current UTM systems and work towards beyond visual line-of-sight (BVLOS) operations which can serve as a foundation for future UTM networks. Flights will be conducted at Daisy Ranch in the Choctaw Nation's IPP flight test area. GE's AiRXOS UTM software alongside UgCS and Mission Planner were utilized to provide aircraft traffic situational awareness using UAS telemetry displayed through each system. This will soon transition to BVLOS testing utilizing the aforementioned technologies to satisfy FAA requirements for sense-and-avoid and then determine if this technology could help provide a minimum viable system for Unmanned Traffic Management.
Dynamic Systems Simulation and Control Using Consecutive Recurrent Neural Networks

In this paper, we introduce a novel architecture to connecting adaptive learning and neural networks into an arbitrary machine’s control system paradigm. Two consecutive Recurrent Neural Networks (RNNs) are used together to accurately model the dynamic characteristics of electromechanical systems that include controllers, actuators and motors. The age-old method of achieving control with the use of the – Proportional, Integral and Derivative constants is well understood as a simplified method that does not capture the complexities of the inherent nonlinearities of complex control systems. In the context of controlling and simulating electromechanical systems, we propose an alternative to PID Controllers, employing a sequence of two Recurrent Neural Networks. The first RNN emulates the behavior of the controller, and the second the actuator/motor. The second RNN, when used in isolation potentially serves as an advantageous alternative to extant testing methods of electro-mechanical systems.
A Bayesian Hierarchical Model for Ranking Aggregation

Rankings—an ordering of items from best to worst—are a common way to summarize a group of items, often at an individual level. These ranks are ordinal data, and should not be acted on by standard mathematical operations such as averaging. Thus, combining these individual rankings to get a consensus can present a difficult challenge. In this paper we present a novel method of combining rankings via a Bayesian hierarchical model, using rankings and their corresponding ratings—an assessment of item quality—to create a data augmentation scheme similar to established literature. Simulations show that this method provides an accurate recovery of true rankings, particularly when the ranking system exhibits clustering within the structure. Additionally this method has the added benefit of being able to describe properties of the rankings, including how preferred one item is to another and the probability that an individual will rank one item higher than another.
A Logistic Predictive Model for Determining the Prevalent Mode of Financial Cybercrime in Sub-Saharan Africa

Cyber fraud is considered as the utilization of the Internet to commit financial-related crimes such as email phishing to craftily retrieve personal and sensitive information from a target. Any crime perpetrated using a computer and/or the Internet is considered as cybercrime, of which cyber fraud is an aspect of. Cybercrime cost has been on the increase, estimated to cost the world over 6 trillion US Dollars by 2021. Its impact on all aspects of life, especially the economy, is very grave and worrying. This study focuses on the analysis of the prevalence of cyber fraud in Sub-Saharan Africa. Google online survey tool was employed to gather responses from users of digital infrastructures for financial transactions, through modes such as mobile banking, SMS USSD codes, ATMs, etc. The questionnaire sought to know if the users had at any time been victims or targets of financial cybercrime and the electronic platforms on which the positive cases of cyber fraud occurred. Machine learning techniques were applied in analyzing the user responses using R Studio version 3.6. A supervised machine learning logistic regression model was developed which showed that two variables representing two modes of financial cybercrime attacks were statistically significant having p-values less than 0.001. The accuracy of the model was found to be 81.38%, which was promising for the generalization of the model. The probability accuracy for ATM cybercrime is 90.37%, while that of Phone mode is 87.76%.
Digital Transformation of the Management of the Polyprofessional Trajectory of the Constituent Entity of Education

The article discloses the problem of digital transformation of the management of the polyprofessional trajectory of the subject of education and the algorithm for its solution. The creation of an integrated information and communication environment of the region on the basis of a digital university is considered as the optimal way of digital transformation of managing the polyprofessional trajectory of the subject of education. In this regard, the basic approaches and methods, regulatory and technological foundations of the operation of the integrated information and communication environment of the region on the basis of a digital university are revealed.
A Secure RGB Image Encryption Algorithm in Optimized Virtual Planet Domain

The primary aim of this chapter is to provide an optimized, secure RGB image encryption algorithm using 4D hyper-chaotic system in Virtual Planet Domain (VPD). We have constructed a new keyspace, and it shows that the keyspace can resist brute force attacks infeasible. We have tested the proposed algorithm on standard test images. We have successfully verified the robustness of the proposed algorithm by using commonly known attacks such as the differential, cropped, noise, and entropy attacks. Finally, we compared the proposed technique with existing algorithms, and the data (shown in tables) confirms that the proposed algorithm is competitive and can resist exhaustive attacks efficiently.