Reviewerly/exHarmony huggingface dataset:
Particle Swarm Optimization

The particle swarm optimization (PSO) algorithm is the second oldest algorithm after the ant colony optimization (ACO) algorithm which started a new algorithms family called swarm intelligence algorithms. In this chapter, we will provide an introduction to the PSO algorithm. We will present the original global version of the PSO algorithm in the pseudo-code form and its source-code in Matlab, and in C++ programming language. Also, we will briefly discuss the local version of the PSO algorithm. We will present two types of neighborhood (geometrical and social) which are used in the local version of the PSO algorithm. We will end this chapter by presenting a step-by-step numerical example of the global version of the PSO algorithm, which could help aid the understanding of potential readers and enable them to carry out their own implementation of the global version of the PSO algorithm in any programming language. Finally, the conclusion section will be shown.
Information Protection: Organization, Roles, and Separation of Duties

The corporate auditing department should be responsible for ensuring compliance with the information protection and security policies, standards, procedures, and guidelines. They should ensure that the organizational business units are operating in a manner consistent with policies and standards, and ensure any audit plan includes a compliance review of applicable information protection policies and standards that are related to the audit topic. A person, organization, or process that implements or administers security controls for the information owners are referred to as information delegates. An information delegate is also any company employee who owns a user ID that has been assigned attributes or privileges associated with access control systems such as Top Secret, RACF, ACF2, etc. This user ID allows them to set system-wide security controls or administrator user IDs and information resource access rights.
Journey Through the Theory of Economic Development

The theory of development economics changed signifcantly from the first attempts of defining the concepts in the 50s until nowadays. The contributions to the development theory are at the same time closely related to the historical context of the underdeveloped and developing countries. This paper tries to facilitate the understanding of the way development economics evolved, by presenting the main contributions to the theory. The research methodology used in this study is analytical, based on the gatherinf of the information, its organisation in a logical way, the presentation and analysis of the most significant ones, and finally the drawing of relevant conclusions. The main findings of this paper are that after five decades of strong ideas and recipes of economic development we have reached a point of uncertainty regarding what is beneficial or harmful for the nations' economic development.&#x0D; Journal of Economic Literature (JEL) classification: O10.
Robust Kernelized Multiview Clustering Based on High-Order Similarity Learning

This paper explores the robust kernelized multi-view clustering (MVC) for nonlinear data.The existing MVC methods aim to excavate the complementary and consensus information from multiview data lies in the linear space for clustering.However, in real-world scenarios, data points usually lie in multiple nonlinear spaces, leading to undesirable clustering results.To this end, we propose a robust kernelized MVC method based on high-order similarity learning (RKHSL), to jointly learn the local structure affinities in original space, the nonlinear affinities in mapping kernel space, and the high-order correlations in tensor space.Specifically, the first-order similarity (FOS) is learned to excavate the local structure affinities and the second-order similarity (SOS) is learned in the high-dimensional kernel space to excavate the nonlinear affinities of data points.Afterwards, the third-order similarity (TOS) based on low-rank tensor is learned to excavate the global consistency from multiple views.Extensive experiments on five commonly benchmark datasets show that the proposed method outperforms state-of-the-art methods in most scenarios and is capable of revealing a reliable affinity graph structure concealed in different data points.
NITK-IT_NLP@TamilNLP-ACL2022: Transformer based model for Toxic Span Identification in Tamil

Offensive Span identification in Tamil is a shared task that focuses on identifying harmful content, contributing to offensiveness.In this work, we have built a model that can efficiently identify the span of text contributing to offensive content.We have used various transformer-based models to develop the system, out of which the fine-tuned MuRIL model was able to achieve the best overall character F1-score of 0.4489.
A Generic Construction of CCA-Secure Identity-Based Encryption with Equality Test against Insider Attacks

Identity-based encryption with equality test (IBEET) is a generalization of the traditional identity-based encryption (IBE) and public key searchable encryption, where trapdoors enable users to check whether two ciphertexts of distinct identities are encryptions of the same plaintext. By definition, IBEET cannot achieve indistinguishability security against insiders, i.e., users who have trapdoors. To address this issue, IBEET against insider attacks (IBEETIA) was later introduced as a dual primitive. While all users of IBEETIA are able to check whether two ciphertexts are encryptions of the same plaintext, only users who have tokens are able to encrypt plaintexts. Hence, IBEETIA is able to achieve indistinguishability security. On the other hand, the definition of IBEETIA weakens the notion of IBE due to its encryption inability. Nevertheless, known schemes of IBEETIA made use of rich algebraic structures such as bilinear groups and lattices. In this paper, we propose a generic construction of IBEETIA without resorting to rich algebraic structures. In particular, the only building blocks of the proposed construction are symmetric key encryption and pseudo-random permutations in the standard model. If a symmetric key encryption scheme satisfies CCA security, our proposed IBEETIA scheme also satisfies CCA security.
Keldysh Institute Preprints in the diagrams of the Science Space system

О р д е н а Л е н и н а ИНСТИТУТ ПРИКЛАДНОЙ МАТЕМАТИКИ имени М.В.Келдыша
Angle steel tower bolt defect detection based on YOLO-V3

The bolts in the angle steel tower are seriously affected by corrosion and loss. This paper proposes a novel detection system based on YOLO-V3 to avoid the danger of traditional manual detection method for the bolt fault detection of the angle steel tower. A multi-scale convolution module is used to replace the ordinary convolution of original YOLO-V3 so as to obtain the spatial characteristics information of different scales in the image, and enhance the detection accuracy. The experimental results show that mAP of the proposed YOLO-SKIP network is 0.91. Our YOLO-SKIP model has achieved the best detection performance on the defective angle steel tower bolt data.
The Effect of Natural Language Processing on the Analysis of Unstructured Text: A Systematic Review

The analysis of the unstructured text has become a challenge for the community dedicated to natural language processing (NLP) and Machine Learning (ML).This paper aims to describe the potential of the most used NLP techniques and ML algorithms to address various problems afflicting our society.Several original articles were reviewed and published in SCOPUS during 2021.The applied approach was retrospective, transversal and descriptive.The data collected were entered into the SPSS statistical software v25 and among the findings, it was determined that the most used NLP technique was the Term frequency -Inverse document frequency (TF-IDF), while the most used supervised learning algorithm was the Support Vector Machines (SVM).Likewise, the predominant deep learning algorithm was Long Short-Term Memory (LSTM).This research aims to support experts and those starting in research to identify the most used algorithms of NLP and ML.
Application of Augmented Reality in Music Education

Music Education has been given in many ways from the past to the present, but the development of technology has been the turning point for this education. The increasing use of mobile devices in recent years has offered people the advantages of using technology in education. 3D applications developed for mobile devices have enabled Augmented Reality (AR) technology to be used in many areas. While the importance of Music Education has been better understood recently, its benefits and necessity continue to be the subject of research by many scientists. Studies have observed that in addition to music education, taking an instrument education contributes to the increasing success of students in other lessons. In this thesis study, musical notation education on the flute was aimed for primary school students, and after the necessary teachings were made, it was ensured that two different music pieces were played over the AR-Flute application.
Convolutional Autoencoder for Image Denoising

Image denoising is a process used to remove noise from the image to create a sharp and clear image. It is mainly used in medical imaging, where due to the malfunctioning of machines or due to the precautions taken to protect patients from radiation, medical imaging machines create a lot of noise in the final image. Several techniques can be used in order to avoid such distortions in the image before their final printing. Autoencoders are the most notable software used to denoise images before their final printing. These software are not intelligent so the resultant image is not of good quality. In this paper, we introduced a modified autoencoder having a deep convolutional neural network. It creates better quality images as compared to traditional autoencoders. After training with a test dataset on the tensor board, the modified autoencoder is tested on a different dataset having various shapes. The results were satisfactory but not desirable due to several reasons. Nevertheless, our proposed system still performed better than traditional autoencoders.&#x0D; KEYWORDS: image denoising, deep learning, convolutional neural network, image autoencoder, image convolutional autoencoder
Gender Classification from Eye Images by Using Pretrained Convolutional Neural Networks

Automatic gender classification from face images has been a popular topic among researchers for a decade. Feature extraction and classification methods are very important to create a successful automatic classification system. Due to the richness of face image datasets today, many successful machine learning and deep learning methods has been implemented. It is very critical to extract accurate features from the datasets to achieve promising classification scores when traditional machine learning methods are used. However, deep learning models have been designed to extract the features automatically from the raw data directly. This also automatize the feature extraction process besides classification. The hidden and unpredictable feature sets can be explored by the deep neural networks which can increase the classification performance comparing to traditional machine learning methods. Convolutional Neural Networks (CNN) as one of the effective classes of deep models have been adopted by many scientists for solving the gender classification problem. It can solve the problem of the fact that facial cues can change from origin to origin which makes the accurate feature extraction harder. There are several state-of-the art pretrained CNN structures which are very successful for image classification problems. The performance of CNNs is generally higher when the number of the input data is high. However, in this study, the success of the pretrained CNN models is investigated when the data is limited. Considering this fact, in this study, rather than using complete face images, only the one eye image regions with eyebrows are used for the gender classification. The performance results present that the best CNN models are NASNetLarge and Xception models.
Moral and legal challenges regarding use of profiling to combat crime.

Viktoria Коrnienko, Oleksii Sheviakov.The problem of the development of the rehabilitation potential of the personality of the legal specialist.The article identifies the features of studying the problem of development of the rehabilitation potential of the personality of a specialist in the legal field.The optimization factors of the rehabilitation potential of the individual for the psychological support of social support of specialists are studied.Theoretical and methodological provisions on the systemic structure of mental phenomena and the concept of development of rehabilitation capacity of the individual are characterized.A set of research methods is considered: information-theoretical, verbal-communicative, psychometric.The essence of the methodological approach based on empirical data, which includes research aimed at harmonizing the psychological state of specialists, is emphasized.The methodical complex of psychological support of development of rehabilitation potential of the person is considered.The role of a social psychologist and the content of social work on the formation of psychological foundations for the development of rehabilitation potential are noted.It is established that the use of theoretical and methodological basis of existing approaches to the psychological support of the development of rehabilitation potential of the individual is insufficient to assess and regulate its psychological state.Therefore, the use of a comprehensive approach to the assessment of psychological states from the standpoint of ideas about the rehabilitation potential of the individual, creates conditions for timely diagnostic (control, evaluation) and corrective (optimization, prevention) work in rehabilitation.
Restrictions on human rights and freedoms in the context of the COVID-19 pandemic: the case law of the European Court of Human Rights.

In the article on the basis of analysis of theoretical foundations and legal and regulatory framework, regulating operational-service and service-combat activities of security forces of Ukraine, the problem questions of organization of activity of National police units for providing public safety and order at mass events have been examined.The conclusion is that nowadays there is the necessity to develop departmental regulatory and legal act, based on the Laws of Ukraine «On National Security of Ukraine», «On National Police» and the Concept of introduction of the Scandinavian model of ensuring public safety and order during mass events, which would be based on the experience of Scandinavian police and ensuring public safety and order holding the Euro 2012, it would include the whole range of questions for providing public safety and order during large events.
BifactorCalc: An Online Calculator for Ancillary Measures of Bifactor Models

The bifactor model allows examining the presence of a total score in a data set by modeling a general factor and two or more specific factors with an orthogonal relationship. These models tend to overestimate the goodness of fit (e.g., CFI, RMSEA, SRMR), hence there exist auxiliary measures that allow examining the dimensionality (ECVGen; ECVSpecific; I-ECV, PUC, ARPB), and reliability (ω, ωS, ωH, ωHS, PRV, H, and FD). The present study describes the operation, mathematical foundations, and application in psychological research of an online calculator called BifactorCalc. The results demonstrate that BifactorCalc is an online, user-friendly, and easy-to-use computer program for the calculation of the different auxiliary measures of bifactor models. It was concluded that the computer tool BifactorCalc is able to calculate the auxiliary measures of bifactor models in three simple steps and generate a path diagram.
Deskilling of Teaching and the Case for Intelligent Tutoring Systems

This essay describes trends in the organization of work that have laid the groundwork for the adoption of interactive AI-driven instruction tools, and the technological innovations that will make intelligent tutoring systems truly competitive with human teachers. Since the origin of occupational specialization, the collection and transmission of knowledge have been tied to individual careers and job roles, specifically doctors, teachers, clergy, and lawyers, the paradigmatic knowledge professionals. But these roles have also been tied to texts and organizations that can disseminate knowledge independently from professionals. Professionals and organizations turn knowledge into texts and tools that enable lay people to access knowledge without the intermediation of professionals or organizations. In the 21st century, one emerging tool for transmitting knowledge is the intelligent tutoring system. This paper examines how technological, epistemic, and economic trends in education are supporting the routinization, proletarianization, and automation of the occupation of teaching, leading to the increasing substitution of intelligent tutoring systems for human instruction.&#x0D; Some trends, such as standardized curricula and testing, both restrict teachers’ professional autonomy and facilitate the creation of pedagogical tools. Other trends reduce teachers’ ability to resist automation. The growth of adjunct teaching and paraprofessional roles in higher education allows organizations to take over and rationalize parts of the traditional teacher role. Faculty evaluations and learning outcomes assessment weaken professional claims to be the sole arbiters of instructional quality and student learning. The widespread use of intelligent tutoring systems also depends on the sophistication of software capable of performing the social-emotional and cognitive roles that educators perform. Eventually, pedagogical software will be able to interactively individualize curricula to the needs and interests of every learner, more cheaply, quickly, and accurately than any human teacher. Assessment of learning will be continuous, and certification of learning will be for specific skills instead of broad area competencies. Intelligent tutoring systems will help transition education from its medieval and industrial-era model to more accessible and flexible continuing education for employment and life enrichment.
Improving Students' Writing Skills by Using Pictures and Picture Method at Junior High School

This study aims to investigate the improvement of students writing skill by using picture and picture method in SMP 21 Simalingkar B. The rational of this study to improve the writing lack on the descriptive text in SMP 21 Simalingkar B. This method of this study was Class Action Research and was carried out in cycle 1&amp;2 in 25 students OF VIII B Medan. The writer collecting data using writing test, documentation, observation and interview. The data was analyzed by using qualitative and descriptive techniques. Based on the recapitulation in the cycle 1 and cycles 2 shown the improvement of students’ writing skill . The percentages in cycle 1 and cycles 2,were 44,8% for pretest, 48,8% for posttest in the cycle 1 and 71,84% for pretest , 81,44% for post test in cycle 2.By on the result of this study, the using of picture and picture method can improve students skills and classroom situation in writing text
TEACHING READING BY VISUALIZATION AS A STRATEGY

Abstract This study aims to determine if visualizing when teaching reading learners is beneficial in reading comprehension for students. One of the ways for teaching reading is visualization. It is the ability of a student to visualize what he or she is reading. Students learn to develop mental pictures to aid comprehension and recall in this method. And students can increase their comprehension of written content by creating mental representations. It increases students' focus, attention to detail, and independent reading skills by assisting them in transforming words into higher-level concepts. And teacher can make students work in pairs and allow them to work through the texts together from different subject areas. It can be inferred that teaching reading to young learners through visualization strategy is effective in reading comprehension for students. Keywords: teaching, reading, students, visualization, comprehension.
Is Face-to-Face Learning Better than Online Learning? Teachers’ voice on the Use of Google Workspace

The study focused on teachers’ voices on the use of Google Workspace (G-Workspace) for education amidst the pandemic. The objects of the study were seven participants. All the participants were teachers at Universitas Muslim Maros. The data was collected using a structured interview. The collected data was analysed using Miles and Huberman’s analysis. The result showed that from the perspectives of teachers, G-Workspace had been used as a group of applications to portray the real offline classroom. The most used applications were G-classroom, G-Meet and G-Mail. Unfortunately, there were some unpopular applications such as G-Calendar and G-Jamborad. G-Workspace promotes integrated applications and collaborative learning. From the result, the integration of the applications gave advantages to the participants as they could open some applications through G-Classroom. Meanwhile, working collaboratively was not the best option as the result of the lack of knowledge on collaborative online learning as well as the incompatible device from students. Notwithstanding, G-Workspace had helped most participants to achieve their learning objectives and they referred to it as helpful applications for learning. However, some participants believed that even though G-Workspace had been a medium for distance learning, they were not able to ensure students’ understanding of the material. Therefore, they chose face-to-face learning as the best way to deliver the teaching material
DEVELOPMENT OF ACADEMIC ATTENDANCE SYSTEM USING VOICE VERIFICATION

Based on the fact that all Human Voices are different, where every individual’s voice contains unique characteristics that can be distinguished from others by using special analysis, voice verification can also be used for biometric recognition of individual. The aim of this project is to develop an automated attendance process captivating and student databases record maintaining using C++ programming and voice verification technique. The Input speech will undergo a series of voice recognition processes. The process starts with Feature extraction to obtain the attribute of voice. The features or specific characteristics for each voice are extracted by linear predictive coding technique (LPC) into feature vectors which later be used in speaker modeling. In speaker modeling stage the feature vectors of each speaker will be then processed by Gaussian Mixture Modeling (GMM) technique to generate speaker models and will be stored in a database. Verification process is done through template matching technique. Based on log-likelihood logic decision (template matching technique) with respect to the FAR (False Acceptance Rate) and FRR (False Rejection Rate), the identity of that speaker is accepted if the match is above the threshold. Once the verification result is accepted, the attendance in MySQL database for that speaker will be auto-updated.