Reviewerly/exHarmony huggingface dataset:
Vision Tracking and Optical Character Recognition for Augmented Reality based Attendance System

Abstract This paper presents a system for smart portable electronic device which is based on computer vision-based techniques for scanning the manually entered data by the user for automatic update on a central database and augmented reality is used for automatic display of updated data to the user. The developed application is in the form of an Android app which is installed in compatible smart portable electronic device. The system for providing an interactive augmented reality-based attendance uses image processing for optical character recognition to identify the characters in the register marked as X’ for absence and any numeric data for presence of the student. For capturing the images, an AR enabled device can be used which is connected to a central database through the internet and the user is made to visualize the augmented information in the form of overlaid virtual content on the real scene.
Image Tampering Localization Based on Superpixel Segmentation

With the rapid development of image editing software such as Photoshop and GIMP, image tampering becomes simpler than ever before without living obvious traces. Photo response non-uniformity (PRNU) is a kind of camera fingerprint, which has always been an effective tool for image forgery detection. By detecting the correlation between camera PRNU and image PRNU, we can judge whether there is tampering. Unlike existing PRNU-based tampering localization algorithms where it estimates correlation based on a sliding window, this paper uses the simple linear iterative clustering (SLIC) superpixel algorithm for non-overlapped segmentation of the detected image. The correlation of PRNU is calculated on the superpixel to determine whether there is tampering. The difference between superpixel and sliding window is that superpixel can accurately depict the edge of the tampered objects, especially for the added object type of tampering. Extensive experimental results demonstrate that the proposed algorithm provides much better localization performance than the state-of-art sliding window based methods with less complexity.
Dynamics of Mental and Emotional State of the Heads of District-Level Internal Affairs Divisions in the Process of Developmental Counselling

The article considers the problem of developmental psychological counselling for the heads of the district-level internal affairs divisions. Modern requirements which are imposed on the senior leadership of internal affairs divisions guide the officials towards continuous self-development, self-realization and modernizing of their psychological, managerial and innovative potential and the psychotechnology of developmental counselling is viewed as one of methods for that. We substantiate the relevance of the study of positive developments in the personalities of the heads of district-level internal affairs divisions in the process of developmental counselling. This psychotechnology (seen as a kind of psychological counselling) facilitates positive changes in the personalities of subjects of management activities (i.e. heads of internal affairs divisions) and optimizes their professional and personal development, helps to resolve psychological problems and crises arising in course of their careers. We also presented the results of the study of the prevailing background functional states of leadership's personalities.
Theoretical Bases for Reducing the Time Complexity of the Rabin Cryptosystem

The paper presents the theoretical bases for reducing the time complexity while implementing the Rabin cryptosystem. This becomes possible due to replacing the computationally costly arithmetic operations of modular multiplication and quadratic residue finding by addition operation. It is proposed to perform these operations using the vector-modular method, in which the numbers are written in the binary number system. Recovery of the number positional representation from its residues will be based on the method of adding the product of modules. This makes it possible to avoid the computationally complex operation of finding the inverse of an element, which is necessary when using the Chinese remainder theorem or Garner’s algorithm. Accordingly, the time complexity of data encryption and decryption is reduced. Analytical and graphical dependences of time complexities of the proposed and classical implementations of the Rabin cryptosystem are shown and analyzed. The proposed approach allows us to build effective information security systems based on the Rabin cryptosystem with increased resistance to cryptanalysis without the loss of speed by increasing the volume of the input unit and the dimensions of the keys. An example of the implementation of the Rabin cryptosystem based on the vector-modular method of modular multiplication and addition operation is given.
An Analysis of Communication Strategies in Speaking Classroom Interaction

This study investigated on the communication strategies at speaking classroom interaction and dominant communication strategies used by students for speaking classroom interaction at the eighth grade of MTs Bina Sejahtera Kuningan. The eight grade of MTs Bina Sejahtera Kuningan was chosen purposively as the sample. A descriptive qualitative methodology was employed in this study, and data were collected through classroom observation and semistructured interview. The data from classroom observation and semi structured-interview were analyzed by transcribing, redacting data, triangulation and drawing a conclusion. The findings showed that there were some types of communication strategies used by students in speaking classroom interaction, message abandonment, use of fillers, self repetition, mime, expressing non-understanding, direct appeal for help, feigning understanding, asking for repetition, and asking for clarification. The dominant communication strategy used by students at speaking classroom interaction was SelfRepetition (33,33%), Use ofFiller (24,82%), Direct Appeal For Help (9,93%), Asking For Clarification (7,80%), Message Abandonment (7,09%), Asking For Repetition (5,67%), Mime (4,26%), and the least number of communication strategy used by students was Feigning Understanding (2,84%). Based on these findings, it is recommended that the students have to improve their speaking skill, especially in pronouncing, mastery of vocabularies and grammar and in speaking class, the teacher should use teaching varieties to make students more active in classroom interaction and overcome students problems and students difficulties in speaking. Key words : Communication strategies, Descriptive qualitative methodology,  Types of communication strategies
CRATOS

Anomaly detection of time series plays an important role in reliability systems engineering. However, in practical application, there is no precisely defined boundary between normal and anomalous behaviors in different application scenarios. Therefore, different anomaly detection algorithms and processes ought to be adopted for time series in different situation. Although such strategy improve the accuracy of anomaly detection, it takes a lot of time for practitioners to configure various algorithms to millions of series, which greatly increases the development and maintenance cost of anomaly detection processes. In this paper, we propose CRATOS which is a self-adapt algorithms that extract features from time series, and then cluster series with similar features into one group. For each group we utilize evolutionary algorithm to search the best anomaly detection methods and processes. Our methods can significantly reduce the cost of development and maintenance of anomaly detection. According to experiments, our clustering methods achieves the state-of-art results. The accuracy of the anomaly detection algorithms in this paper is 85.1%.
Research on Image Generation Based on Guide Map

Given an image of a person performing certain actions, an image of action that the person has not yet performed can be generated. We can think of this task as an image generation task. This is an image-to-image task of posture transfer. One of the drawbacks of the image generation task is that the quality of the image is poor. The main reason for this problem is that there is no information guidance for the image generation. In order to solve this problem, we have added a pose map as a guide for image generation.
Deep Video Understanding of Character Relationships in Movies

Humans can easily understand storylines and character relationships in movies. However, the automatic relationship analysis from videos is challenging. In this paper, we introduce a deep video understanding system to infer relationships between movie characters from multimodal features. The proposed system first extracts visual and text features from full-length movies. With these multimodal features, we then utilize graph-based relationship reasoning models to infer the characters' relationships. We evaluate our proposed system on the High-Level Video Understanding (HLVU) dataset. We achieve 53% accuracy on question answering tests.
Unsupervised Learning of Deep-Learned Features from Breast Cancer Images

Detecting cancer manually in whole slide images requires significant time and effort on the laborious process. Recent advances in whole slide image analysis have stimulated the growth and development of machine learning-based approaches that improve the efficiency and effectiveness in the diagnosis of cancer diseases. In this paper, we propose an unsupervised learning approach for detecting cancer in breast invasive carcinoma (BRCA) whole slide images. The proposed method is fully automated and does not require human involvement during the unsupervised learning procedure. We demonstrate the effectiveness of the proposed approach for cancer detection in BRCA and show how the machine can choose the most appropriate clusters during the unsupervised learning procedure. Moreover, we present a prototype application that enables users to select relevant groups mapping all regions related to the groups in whole slide images.
Towards Loss Balance and Consistent Model in Self-supervised Monocular Depth Estimation

Recently, self-supervised methods based on Convolutional Neural Networks (CNN) have achieved remarkable success in monocular depth estimation. To obtain higher quality depth maps, some of these approaches leverage traditional schemes to compute rough depth maps as proxy labels and adopt classic regression loss functions to minimize the differences between network-predicted depth maps and proxy labels. However, at proxy labels with large depth values, these methods suffer from a loss imbalance problem. To address this limitation and further improve the network performance, this article offers three key contributions. Firstly, a novel regression loss function is proposed, which can alleviate the loss imbalance problem and better handle rough proxy labels. Secondly, a dynamic mask is designed to accelerate network convergence. Thirdly, an innovative consistency loss is introduced, which can produce a more accurate and consistent model by maintaining consistency between the produced depth maps of each input image and its mirror. The effectiveness of our contributions is demonstrated by a series of ablation studies. Extensive experiments on KITTI dataset reveal that our approach achieves state-of-the-art results.
Experimental and Theoretical Scrutiny of the Geometric Derivation of the Fundamental Matrix

In this paper, we prove mathematically that the geometric derivation of the fundamental matrix F of the two-view reconstruction problem is flawed. Although the fundamental matrix approach is quite classic, it is still taught in universities around the world. Thus, analyzing the derivation of F now is a non-trivial subject.
Understanding Legacy Systems in the Light of Grounded Theory

Software systems developed over a long period of time require constant maintenance to remain useful to organizations. These systems, which tend to degrade and cause maintenance problems, are usually called legacy systems. The management of legacy systems involves understanding its characteristics, such as what it is, what problems it presents and which evolution strategies. From the literature perspective, there is a wide and diversified understanding of these characteristics. This article is intended to provide a more concise understanding of such features related to legacy systems. To achieve this goal, textual excerpts from the literature were collected and analyzed according to Grounded Theory (GT) procedures, resulting in a concise understanding supported by visual models. The results show that systems developed with obsolete technologies, some decades ago, that have incomplete or missing documentation, among others, are defining characteristics for a system to be considered legacy. Some characteristics cause financial problems, difficulty in maintaining the code, among others. It was also observed that there are several evolution strategies and that migration of legacies to the platform is a trend. It is hoped that this study will serve as support for the identification of elements that indicate whether a system is or is becoming a legacy, as well as the possible solutions to evolve them. In addition, the detailed data analysis process with GT procedures, which used a collaborative coding process with a tool developed for this purpose, can support researchers who need to use this type of method in their research in various areas of knowledge.
Method of Comparison of Neural Network Resistance to Adversarial Attacks

The vulnerability of neural networks to adversarial attacks has long been revealed. However, the structure of neural networks is not given due attention during the attack. The article deals with the impact of different parameters of a neural network on its resistance to adversarial attacks. The main purpose of this research is to determine which parameters increase resistance to attacks. The way by which neural networks can be compared has been proposed. Several neural networks were selected for comparison and a number of adversarial attacks were conducted on them. As a result, certain conditions were identified under which the attack took place over a longer time. It was also found that different changes in neural network parameters were required to protect against different attacks.
Quantum speed limit time in a relativistic frame

The evolution of quantum states in a relativistic scenario is a meaningful and practically relevant problem in the framework of quantum physics simulation in a noninertial frame. Here, we investigate the roles of relativistic effect induced by two relatively accelerated bosonic observers on the speed of evolution of a quantum system coupled with amplitude damping channels. We find that the relativistic effect speed-up the quantum evolution of amplitude damped system to a uniform evolution for a damping strength pτ≲pτc0, where pτc0 is a critical damping parameter before which the relativistic effect increases the speed of evolution of the damped system. Moreover, we point out a non-monotonic behavior of the quantum speed limit time (QSLT) with acceleration in the damping limit pτc0≲pτ≲pτc1, where pτc1 is the critical damping parameter beyond which the relativistic effect slows down the speed of evolution of the system. In this range, the relativistic effect first speeds-up and then slows down the quantum evolution process of the damped system. For a damping strength pτc1≲pτ, we observe a monotonic increasing behavior of QSLT, leads to slow down the quantum evolution of the damped system. In addition, we examine the roles of the relativistic effect on the speed limit time for a system coupled with the phase damping channels.
Development of computer aided manufacturing learning module for Magelang vocational high school students

Abstract This study aims to develop computer aided manufacturing (CAM) learning modules for students, find out the feasibility of learning modules for students, and find out student assessments of modules. The research method used is 4D (Define, Design, Develompent, Dissemination). Data collection using a questionnaire. The results showed the feasibility of teaching materials by material experts who get a percentage of 84.17% with a very decent category, from CNC learning practitioners get a percentage of 85.83% with a very feasible outcome category, and assessment of students in class XII machining at Vocational High School 1 Magelang obtained a percentage of 85.28% which is included in the very feasible category. Based on the results of the assessment by material experts, CNC learning practitioners, and student assessments, this module is included as a very feasible use as teaching material for vocational students.
Exploring recurrent frames in written Chinese

Patterning in frames (i.e., discontinuous word sequences with at least one variable slot) involves both syntagmatic co-occurrences and paradigmatic variations, and this has received considerable attention in phraseological research. However, few studies have attempted to extract the full set of recurrent frames in written Chinese. This study sets out to do so based on an enormous Google database. Common quantitative measures adopted in previous studies are reconsidered. Owing to the considerable quantity of data, some of these measures become less sensitive. High-frequency frames are categorised according to the grammatical category of their components. The results lend cross-linguistic support to previous major findings and show language-specific distributional patterns. Moreover, high-frequency frames are categorised according to their dominant syntactic role in context. Finally, the semantic aspect of Chinese frames is addressed. A semantically hierarchical representational network of frames is established, and significant structural and semantic correlations are identified.
Hybrid face recognition under adverse conditions using appearance‐based and dynamic features of smile expression

Although recent deep-learning-based face recognition methods give remarkable accuracies on large databases, their performance has been shown to degrade under adverse conditions (e.g. severe illumination and contrast variations; blur and noise). Under such conditions, soft-biometric features such as facial dynamics are expected to increase the performance if they are used together with appearance-based features. We propose a novel hybrid face recognition, which uses appearance-based features extracted using deep convolutional networks and statistical facial dynamics features extracted from facial landmark positions during smile expression. We evaluated the performances of three different state-of-the-art pre-trained deep convolutional neural networks (DCNNs) under a variety of severe image distortions with different parameters. The experimental results show that, although the face recognition performance using only DCNN-based features drops significantly under adverse conditions, the utilization of facial dynamics features together with DCNN-based features can compensate for the performance loss and increase the accuracy significantly. We believe the proposed system can be useful when face recognition is performed using videos obtained from systems, which may contain blurry and noisy images with a wide range of illumination variations.
Online education: problems and prospects of development

The current situation with the Covid-19 coronavirus pandemic has made adjustments to all aspects of society's life. The sphere of education, including higher and postgraduate education, has not escaped serious and forced transformations-in order to ensure the health of students at all levels of training, organizations of higher and postgraduate education, together with the Ministry of education and science, were forced to implement a universal and complete transition to the format of training using distance technologies. In this regard, certain issues arose with the compliance of the regulatory framework with the current conditions, opportunities and needs of educational organizations and regulatory authorities. Certain problems were identified with educational resources and tools, the readiness and competence of the teaching staff and administrative staff of higher and postgraduate education organizations, as well as public authorities in the field of education to work in a distance format, issues of academic integrity and dishonesty, the process of conducting current classes, intermediate and final certification, proctoring, as a control procedure for an online exam or testing. Some issues are the quality of classes, educational materials and resources used in remote educational technologies, the readiness of students and teaching staff to work in a remote (online and offline) format.
An Evolutionary Hybrid Feature Selection Approach for Biomedical Data Classification

Feature selection is an important function/subject in machine learning. It involves separating the relevant features of the data set and reducing its dimension by eliminating unnecessary data, leading to predictive performance. To this end, researchers use specific search methods to find an optimal subset of features. The aim of this study is developing a hybrid algorithm according to simulated annealing (SA) and grey wolf optimizer (GWO) to be applied in feature selection for biomedical data. Grey wolf algorithm optimizer is an innovative, bio-inspired method of optimization and, as the name suggests, reproduces the actual pattern of how grey wolves hunt in their natural habitat. Two feature selection methods (BGWO1-SA and BGWO2-SA) are presented here. For greater intensification of the suggested algorithm, the SA algorithm receives the inputs of the wolves' updated position in the last phase of both above-mentioned approaches. The proposed methods are compared with four competitors: particle swarm optimization, genetic algorithms, and two versions of GWO algorithm. The assessments were based on a set of challenging biomedical benchmarks, and the results showed that the presented methods outperform their rivals.
Text Recognition for Automated Test Execution in Interlocking: A Deep Learning Approach

In this paper, we present a deep learning character recognition algorithm based on multi-level segmentation. It can improve the accuracy of recognition of button characters in the interlocked upper computer interface, which is of great significance to the design of the automatic test execution in the interlocking system. We first analyze the characteristics of characters in the interlocked upper computer interface and combine various methods to segment characters at multiple levels. Then, we build a deep learning character recognition model based on CNN to accurately recognize characters. Finally, we applied our algorithm to recognize the characters of the interfaces in a real interlocked upper computer software and made a comparison with other algorithms. The obtained results show the feasibility and advantage of our approach.