Reviewerly/exHarmony huggingface dataset:
Automatic Attendance Management System Using Face Detection and Face Recognition

Attendance plays a major role in every education system. Taking attendance of students manually can be a great burden for teachers. It may cause many problems like loss of time, repetition, incorrect markings, and difficulties in marking them. To avoid this, there is a need to design an automatic system that overcomes the issues with the traditional attendance system. There are many automatic methods available for this purpose like fingerprint systems, RFID systems, face recognition systems and iris recognition systems, etc. But among these Face Recognition proved to be more efficient. The main objective of this paper is to propose a model that captures images from videos, detect and recognize the faces, predict the recognized face, and then marks attendance. In this work, a basic step has been performed which uses fifteen classes from LFW Dataset and faces are detected, recognized, and then prediction is done on a randomly selected image from the used dataset. This system uses a combination of Multi Task Cascaded Neural Network (MTCNN) algorithm along with FaceNet that can be used to detect faces and extract facial features from images. SVM is used to predict the face of the person from the image. The proposed system obtains a classification accuracy of 99.177% for the training set and 100% on the test set. The accuracy, precision, recall, and F1-score are computed.
Siamese Guided Anchoring Network for Visual Tracking

Recently, the Siamese Region Proposal Network (SiamRPN) has been widely explored in tracking and achieved remarkable performance. However, the existing SiamRPN-based method uses a predefined and highly dependent on prior knowledge anchor, which limits the tracking accuracy. Besides, when the target changes drastically, the anchor box obtained by the SiamRPN-based method also has some negative samples, which leads to a decrease inaccuracy. To address these issues, this paper proposes a siamese guided anchoring network for visual tracking, which can obtain more representative anchors by estimating the position and shape of the target, reducing the adverse effects of negative samples. At the same time, a feature adaption module is proposed to adapt to the target scale change for learning more discriminative and useful features and achieving more accurate visual tracking. Extensive experiments on challenging OTB100 and VOT2018 datasets demonstrate the competitive performance of the proposed algorithm in comparison with the state-of-the-art trackers.
Phishing Detection Using Computer Vision

Phishing is a cyber-crime wherein innocent web users are trapped into a counterfeit website, which is visually similar to its legitimate counterpart, but in reality, it is fake. Initially, users are redirected to phishing websites through various social and technical routing techniques. Users being ignorant about the illegitimacy of the website, provide their personal information such as user id, password, credit card details, bank account details to name a few. These details are stolen by the phishers and later used for either financial gains, or to tarnish a brand image or even more grave crimes like identity theft. Many phishing detection and prevention techniques are proposed in the literature; however, there is much scope in the cyber-security world with the advent of smart machine learning and deep learning methods. In this research, we explored computer vision techniques and build deep learning and machine learning classifiers to detect phishing website and their brands. Some of the experiments include Transfer Learning and Representation Learning techniques by utilizing various off-the-shelf Convolutional Neural Network (CNN) architectures to extract image features. It is observed that DenseNet201 outperforms all experiments conducted as well as the existing state-of-the-art on the dataset used, proving the hypothesis that Convolutional neural networks are an effective solution for extracting relevant features from phishing webpages for phishing detection classification.
Application of Machine Learning Classifiers for Predicting Human Activity

Involving machine learning in recognizing human activities is a widely discussed topic of this era. It has a noticeable growth of interest for implementing a wide range of applications such as health monitoring, indoor movements, navigation and location-based services. This paper compares the performance of various machine learning algorithms in the domain of human activity recognition. Data of different aged people is collected using a custom setup and custom hardware. The observed data are modeled using machine learning and neural network. As recorded human motions have variations and complexity, four dataset reduction techniques are used to manipulate the results. Best accuracy is obtained for SVM classifier with 99% accuracy and after applying PCA and SVD techniques the accuracy percentages increased to 100%. On the other hand, worst accuracy is obtained for Naive Bayes classifier before and after applying LDA technique for 100 components. The accuracy percentages are 77% and 98% respectively.
Intelligent Identification and Resolution of Software Requirement Conflicts: Assessment and Evaluation

Considerable research has demonstrated how effective requirements engineering is critical for the success of software projects. Requirements engineering has been established and recognized as one of the most important aspects of software engineering as of late. It is noteworthy to mention that requirement consistency is a critical factor in project success, and conflicts in requirements lead to waste of cost, time, and effort. A considerable number of research studies have shown the risks and problems caused by working with requirements that are in conflict with other requirements. These risks include running overtime or over budget, which may lead to project failure. At the very least, it would result in the extra expended effort. Various studies have also stated that failure in managing requirement conflicts is one of the main reasons for unsuccessful software projects due to high cost and insufficient time. Many prior research studies have proposed manual techniques to detect conflicts, whereas other research recommends automated approaches based on human analysis. Moreover, there are different resolutions for conflicting requirements. Our previous work proposed a scheme for dealing with this problem using a novel intelligent method to detect conflicts and resolve them. A rule-based system was proposed to identify conflicts in requirements, and a genetic algorithm (GA) was used to resolve conflicts. The objective of this work is to assess and evaluate the implementation of the method of minimizing the number of conflicts in the requirements. The methodology implemented comprises two different stages. The first stage, detecting conflicts using a rule-based system, demonstrated a correct result with 100% accuracy. The evaluation of using the GA to resolve and reduce conflicts in the second stage also displayed a good result and achieved the desired goal as well as the main objective of the research.
Multi-user Fully Homomorphic Encryption Scheme Based on Policy for Cloud Computing

Cloud computing, a modern application that is very commonly utilized, has garnered considerable interest from researchers and developers. Data privacy security is the most urgent issue related to cloud computing. Studies have shown that fully homomorphic encryption (FHE) technology, which is related to traditional encryption and ciphertext evaluation, is an effective way of securing privacy in the cloud. This study proposes a policy-based, multi-user, FHE algorithm (PB-MUFHE) that accounts for user diversity in the cloud environment, in which, the encrypted data is set under an appropriate access policy, the key to which is a set attribute. Not only is homomorphic evaluation supported for ciphertexts among several users, but also fine-grained access control and sharing between users are supported. The security of the PB-MUFHE scheme was tested based on learning with errors (LWE) problems, and IND-CPA security analysis in the generic bilinear group random oracle model further verified the proposed algorithm's effectiveness. Performance assessment demonstrated that, PB-MUFHE can efficiently implement fully homomorphic evaluation of ciphertext, and effectively support access control and shared multi-user capability.
Indonesian Covid-19 Future Forecasting Based on Machine Learning Approach

Since the Coronavirus disease (COVID-19) outbreak emerged in China, several studies attempt to predict COVID-19 as an effort to reduce the spread of the virus. Indonesia is one of the countries with a Covid-19 death rate is the highest in Southeast Asia. These need early preventive measures, One of them is by forecasting the number of the COVID-19 cases. this is very important to be taken to determine the number of maximum cases as a prepare health facilities effort in Indonesia and preparations for reopening economic activities in Indonesia. The focuses of this study are forecasting future COVID-19 in Indonesia using the Multiple Linear Regression model. The data set were taken from the website: https://data.humdata.org/dataset/indonesia-Covid-19-cases-recoveries-and-deaths-perprovince. The tools using Python. The result of this study is the MLR model has high accuracy in the prediction model. This is shown by several results, among others, first the P-value smaller than α (0.005). Second, the F value is higher than the F table, where the F value is 901.665 and the F table is 3,170234. Third, the t-value of affected and recovered cases is higher than the t table. Where t-value is both of them are 4003 and 15529, meanwhile t table is 1,969. The last R2 value is 0.9999 and the Mean standard Error (MSE) is 0.0048 The predicted results shown have an average difference in results of 0.037%.
Face Detection With Face Mask for COVID-19 Pandemic Using Neural Networks

The COVID-19 virus can be spread through contact and contaminated surfaces; therefore, typical biometric systems like password and fingerprint are unsafe. Face recognition solutions are safer without any need of touching any device. During the COVID-19 situation as all of the people are advised to wear masks on their faces, the existing face detection technique is not able to identify the person with face occlusion. The fraudsters and thieves take advantage of this scenario and misuse the face mask, favoring them to be able to steal and commit various crimes without being identified. Face recognition methods fail to detect or recognize the face as half of the face is masked and the features are suppressed. Face recognition requires the visibility of major facial features for face normalization, orientation correction, and recognition. Thus, the chapter focuses on the facial recognition based on the feature points surrounding the eye region rather than taking the whole face as a parameter.
Neural Network Recognition of Russian Noun and Adjective Cases in the Google Books Ngram Corpus

The article proposes a solution to the problem of automatic recognition of Russian noun and adjective cases in the Google Books Ngram corpus. The recognition was performed by using information on word co-occurrence statistics extracted from the corpus. Explicit Word Vectors composed of frequencies of ordinary and syntactic bigrams that include a given word were fed to the input of the recognizer. Comparative testing of several types of vector representation and preliminary data normalization were carried out. The trained model was a multi-layer perceptron with a softmax output layer. To train and test the model, we selected 50000 adjectives and 50000 nouns that were most frequently used in the Google Books Ngram Russian subcorpus between 1920 and 2009. Parts of speech and cases were determined using the OpenCorpora electronic morphological dictionary. The recognition accuracy of the cases obtained using the trained neural network model was 96.45% for the nouns and 99.63% for the adjectives.
Semi-supervised Graph Edge Convolutional Network for Anomaly Detection

In recent years, with deep learning development, graph-based deep anomaly detection has attracted more and more researchers’ attention due to graph data’s strong expression ability. However, at present, graph-based methods mainly focus on node-level anomaly detection, while edge-level anomaly detection is relatively minor. Anomaly detection at the edge level can distinguish the specific edges connected to nodes as detection objects, so its resolution granularity is more detailed than that of the node-based method. Second, the rules of anomalies are challenging to learn. At present, most of the algorithms adopt the unsupervised method to train the model. As a result, the detected result is likely to be noise data. In this paper, we propose a Graph Edge Anomaly Detection model based on a Semi-supervised auto-encoder (GEADS). In this model, we first adjust the traditional mini-batch training strategy to train the model on a large-scale graph. It improves the scalability of the model. Second, we design an edge convolutional neural network layer to realize the fusion of edge neighborhood information. We take the reconstruction error as the evaluation criterion after stacking multiple edge convolutional neural network layers that encode and decode the edges. Third, the few abnormal samples with known labels are utilized to guide the model’s parameter optimization process. While ensuring the generalization ability of the model, it also improves the pertinence to specific anomalies. Finally, we show the effectiveness of the proposed algorithm through experiments on two real-world datasets.
AS relationships inference from the internet routing registries

We present a methodology to infer business relationships between ASes using routing polices stored in the Internet Routing Registries (IRR), which are a set of databases used by ASes to register their inter-domain routing policies. We show that the overall accuracy of our algorithm is comparable (95% for p2c, 92% for p2p links) to the existing algorithms, which infer AS relationships using BGP AS paths. We highlight that the IRR is a strong complementary source for better understandings of the structure, performance, dynamics, and evolution of the Internet since it is actively used by a large number of operational ASes in the Internet.
From Anthropocene to Artificial Intelligence? Challenges of Machine Learning for Science, Life, and Society

In a complex dynamical system of natural and civilizational influence, which is sensible to local perturbations and may undergo chaotic destabilization due to nonlinear interactions, artificial intelligence (AI), data mining, and modern sensor technology may provide the urgently needed early monitoring systems. However, these tools, when powerful and self-organizing, may themselves pose ecological and ethical problems. Therefore, we need more global governance of an AI-supported socioeconomic and Earth system to keep the balance of the whole system.
Nikonorova E.V. Maria Ivanovna Akilina — Scientist, Friend, and Ally (1953—2021)

The article describes contribution to the library science and scientific activity of the Russian State Library (RSL) of the famous librarian Maria Ivanovna Akilina (April 13, 1953 — June 04, 2021). Since 1989, first as senior research scientist in the departments of the Russian State Library, and then in the scientific research department of library science, she worked her way up from leading researcher to the head of the department. Together with her colleagues — library scientists, she made the great contribution to the development of problems of the general theory of library science, the organization of practical activities of libraries, scientific and methodological assistance to libraries of the country. In the scientific research department of library science, there was created the all-Russian system for monitoring library collections; there were studied the issues of standardization of library science; and the work was carried out to create the Electronic Library of the RSL. The department developed and implemented the methodology for preparing the first fundamental unique reference publication on library science in our country — the “Library Encyclopaedia”, which was published in 2007 by the “Pashkov Dom” publisher. M.I. Akilina was a member of the editorial board of this publication; she also made a great contribution to the work as an editor and author of the articles. Together with the staff of the department from 2002 through 2013, she actively participated in the publication of the scientific-practical collection “Librarianship — the 21st Century” — the supplement to the journal “Bibliotekovedenie” [Russian Journal of Library Science], and also became one of the compilers of the collection “Library Research in the System of Postnonclassical Science”. M.I. Akilina managed to create an atmosphere of creativity and scientific research in the department, which became a significant factor in the development of interdisciplinary approaches to the study of librarianship, its various aspects and research practices. Since 2015, M.I. Akilina worked as a leading research scientist in the Centre for the study of problems of library development in the information society. She is the author of more than 100 scientific papers, major part of which is devoted to the scientific and methodological activities of libraries and general theoretical problems of library science; these are the publications about national, central, mass rural and other libraries. When communicating with colleagues, she always took into account not only their achievements, but also the ability to be honest both in scientific work and in human relations. Her contribution to the library scientific research will undoubtedly become part of the research heritage of library science.
Treatment of Dye Waste Water using Moving Bed Biofilm Reactor and Granular Activated Carbon with Neem Leaf Powder

Abstract In recent days, the waste productions from industries are increased and direct discharge of wastes causes various pollution to the environment. Wastewater treatment in textile and dye industry mainly involves treatment of highly colored wastewater containing variety of dyes in different concentrations. The dyeing industries using different types of pigments and dyes of various concentration during their several production steps. These industries discharge a wastewater which contains organic substance, high colour with high COD and low concentration of BOD. This dye waste water are directly discharge in to the streams and thus contaminate the groundwater. To treat the wastewater, which is discharge from the dyeing industries a simple physio-chemical treatment is not sufficient for the removal of pollutants. So that a biological treatment followed with a physio-chemical treatment have been required. In this study, the experiment was carried out to reduce the organic substance, high colour, COD, BOD, and Heavy metals from dye wastewater using the laboratory scale Moving Bed Biofilm Reactor followed with Granular activated Carbon bed (GAC with NLP-MBBR) with Neem leaf powder. A various thick granular activated carbon bed was used for adsorption technique and for biological treatment of wastewater. The GAC with 8cm thick is layered followed by Ennore sand with 3 grades with 4cm thick equally. Then the MBBR was operated continuously with various retention times. The polypropylene type bio balls were used for microorganism attachment in MBBR. The outlet water from the MBBR are treated using membrane process. Membrane process using moving membranes reduces the pH, organic contents, heavy metals, COD, BOD, turbidity etc., The final effluent water is suitable for Irrigation.
Hierarchical Color Quantization with a Neural Gas Model Based on Bregman Divergences

In this paper, a new color quantization method based on a self-organized artificial neural network called the Growing Hierarchical Bregman Neural Gas (GHBNG) is proposed. This neural network is based on Bregman divergences, from which the squared Euclidean distance is a particular case. Thus, the best suitable Bregman divergence for color quantization can be selected according to the input data. Moreover, the GHBNG yields a tree-structured model that represents the input data so that a hierarchical color quantization can be obtained, where each layer of the hierarchy contains a different color quantization of the original image. Experimental results confirm the color quantization capabilities of this approach.
Implementing a Low-Complexity Steganography System on FPGA

Data hiding is an information security transmission method that hides secret information in the associated acceptable multimedia system carrier, which is of great significance to data security. However, its application on image transmission hardware systems is limited due to its complex algorithm and computation burden for processing. To tackle this problem, a simple data hiding algorithm in images is proposed in this paper. Then, the low-complexity steganography system with images as host signals is implemented based on the proposed algorithm. As a result, it can guarantee the PSNR of the carrier image that is greater than 42dB with 26% of the AVG hiding capacity, and the secret information is recovered without distortion. Moreover, the system has been implemented on FPGA which can save resources and realize real-time data processing.
What Kind of Employees’ Team is Necessary for Industrial Digital Transformation? Theoretical and Practical Analysis

The article considers cross-functional teams as an alternative to the traditional team-building approach in industrial digital transformation. Based on the contextual analysis of the reasons for creating cross-functional teams, their polyphonic nature and exclusivity signs are revealed. The paper demonstrates particular importance of knowledge sharing in cross-functional teams for efficient work. Knowledge sharing among employees serves as a source for obtaining necessary and useful information and a condition for the successful functioning of cross-functional teams. The purpose of the study was to perform a comparative analysis of the influence of organizational and managerial factors on knowledge sharing in cross-functional teams. The academic environment of two universities in Russia and Germany was used as an example to determine the obstacles and conditions of this process. In the study, two groups of employees from Russian (Ekaterinburg) and German (Brandenburg an der Havel) universities were examined. The paper determines the degree of influence of various organizational and managerial factors on the intensity of knowledge sharing in cross-functional teams under the national context.