Reviewerly/exHarmony huggingface dataset:
Anomaly detection in the probability simplex under different geometries

Abstract An open problem in data science is that of anomaly detection. Anomalies are instances that do not maintain a certain property that is present in the remaining observations in a dataset. Several anomaly detection algorithms exist, since the process itself is ill-posed mainly because the criteria that separates common or expected vectors from anomalies are not unique. In the most extreme case, data is not labelled and the algorithm has to identify the vectors that are anomalous, or assign a degree of anomaly to each vector. The majority of anomaly detection algorithms do not make any assumptions about the properties of the feature space in which observations are embedded, which may affect the results when those spaces present certain properties. For instance, compositional data such as normalized histograms, that can be embedded in a probability simplex, constitute a particularly relevant case. In this contribution, we address the problem of detecting anomalies in the probability simplex, relying on concepts from Information Geometry, mainly by focusing our efforts in the distance functions commonly applied in that context. We report the results of a series of experiments and conclude that when a specific distance-based anomaly detection algorithm relies on Information Geometry-related distance functions instead of the Euclidean distance, the performance is significantly improved.
Conceptualizing Organizational Citizenship Behavior and Learning Organization in the Labor Sector

Purpose – This study aims to investigate and evaluate the impact of Organizational Transformational Leadership and Work Engagement on Citizenship Behavior in mediating Learning Organization. Methodology – A saturated sample of 96 employees were used, and the quantitative analysis employed the questionnaire collection method using the Smart–PLS Structural Equation Modeling. Findings – Work Engagement had a significant impact on Organizational Citizenship Behavior (OCB), while Transformational Leadership had a negative impact. Work Engagement had a significant positive effect on Learning Organization, which in turn had a significant negative effect on OCB. Organizational Citizenship Behavior could be influenced by Transformational Leadership and Work Engagement through the mediation of Learning Organization. Originality – This study examined the experiences of workers who were more objective in giving their perceptions due to the presence of serious problems in Work Engagement. Previous research used workers as a measure of Work Engagement success but were less objective, particularly in the manufacturing industry where work-related problems were absent.
VERBAL DEVIATIONS IN CONTEMPORARY ENGLISH SOCIAL MEDIA PLATFORMS

The harnessing development of technology has drastically modified languages that people are using in all corners of the world. It is out of the question that world languages are constantly evolving to meet the needs and requirements of the society. Nowadays social media platforms have undoubtedly become an indispensable part of daily communication. Undeniably various platforms of social media attract millions of people with different ethnic, linguistic, racial and religious backgrounds to interact and take part in various discussions in blogs, microblogs, chat groups, discussion boards, forums, etc. Over the last 20 years Internet mediated communication has generated many grammatical structures and lexical items that need to be studied and redefined. With the advent of new technologies new linguistic deviations have emerged in social media platforms. It is no wonder that scientists are holding opposing thoughts and conflicting viewpoints of the same or similar phenomena. Being the most prevalent language of social media and having the highest frequency of usage on the Internet domain the English language has underwent large-scale changes and alternations. These non-standard forms are noted in all levels of language. Over the time linguistic deviations have the tendency to become standard forms. The latter may lead the English language to changes. The current article discusses the marked variations of English verbal system in contemporary English social media platforms.
Object Detection Using Machine Learning : A Comprehensive Review

This research paper provides a comprehensive review of the advancements in object detection using machine learning techniques. Object detection plays a crucial role in computer vision applications, enabling the identification and localization of objects within images or videos. With the rapid growth of image and video data, machine learning approaches have become increasingly popular due to their ability to learn and recognize objects with high accuracy. This paper aims to explore the various machine learning algorithms and methodologies employed in object detection, including traditional methods and deep learning-based approaches. The findings of this review will provide researchers and practitioners with valuable insights into the advancements, challenges, and future directions in object detection using machine learning.
Sinc‐attention feature extraction for trivial‐event based speaker verification

Abstract Human speech contains trivial events that are non‐subjectively controlled and are limited by physical characteristics. These events are represented by a series of extremely short utterances, such as the ‘hmm’ phrase used for expressing doubt or confirmation. In speaker verification (SV), such events can be a robust choice to verify the real speaker from disguised speech since they are less affected by the randomness of pronunciation. However, trivial events like ‘hmm’ contain little linguistic information and are extremely short, thus the performance of SV systems will decrease drastically. In this letter, a Sinc‐Attention feature extraction method is proposed to extract more discriminative speech features from speech signals to achieve a robust SV system for trivial events. Learnable filters are utilized to obtain low‐level representations of the speech for SV. Moreover, a novel adaptive weighting of features inspired by attention mechanisms is proposed, which effectively improves the representation capability of speaker features. The experiments on different models prove that our method helps SV systems achieve a lower equal error rate (EER) than hand‐crafted feature‐based systems.
How to shoot yourself right with a smartphone?

Abstract This article presents a new use case of using handheld augmented reality to set up a smartphone camera for taking a self-photograph needed for evaluating the user’s sports exercise. The problem addressed is that of settings up the rear smartphone camera so that users can take photographs of themselves performing sports poses without the need for assistance from another person. The proposed solution uses an augmented reality avatar with the same body properties as the user, which is placed into the scene so that the user can set up the camera to best capture the avatar. We also propose a straightforward mode of interaction for placing the avatar into the scene and setting up the camera for the desired purpose. We conducted a user study to explore the usability and user experience of the proposed solution. The results showed that the augmented reality visual aids, especially the avatar, enhanced the effectiveness of taking the self-photograph and that users perceived augmented reality elements favorably.
Moving Object Detection Based on Ghost and Shadow Removal

Abstract With the rapid development of information technology and the popularization of security awareness, the society’s demand for intelligent video surveillance is higher and higher. As a key step, moving object detection with a long history of research and continuously changed research methods has become a frontier topic in various disciplines. Aiming at the removal of ghosts, this paper proposes an improved visual background extractor algorithm (Vibe), and then combines texture information to remove shadows which can achieve the purpose of removing heavy shadows and ghosts. Finally, achieving a good video moving target detection.
Gradient Structure Information-Guided Attention Generative Adversarial Networks for Remote Sensing Image Generation

A rich and effective dataset is an important foundation for the development of AI algorithms, and the quantity and quality of the dataset determine the upper limit level of the algorithms. For aerospace remote sensing datasets, due to the high cost of data collection and susceptibility to meteorological and airway conditions, the existing datasets have two problems: firstly, the number of datasets is obviously insufficient, and, secondly, there is large unevenness between different categories in datasets. One of the effective solutions is to use neural networks to generate fake data by learning from real data, but existing methods still find difficulty in generating remote sensing sample images with good texture detail and geometric distortion. To address the shortcomings of existing image generation algorithms, this paper proposes a gradient structure information-guided attention generative adversarial network (SGA-GAN) for remote sensing image generation, which contains two innovative initiatives: on the one hand, a learnable gradient structure information extraction branch network can be added to the generator network to obtain complex structural information in the sample image, thus alleviating the distortion of the sample geometric structure in remote sensing image generation; on the other hand, a multidimensional self-attention feature selection module is proposed to further improve the quality of the generated remote sensing images by connecting cross-attentive modules as well as spatial and channel attention modules in series to guide the generator to better utilize global information. The algorithm proposed in this paper outperformed other methods, such as StyleGAN-XL and FastGAN, in both the qualitative and quantitative evaluation, whereby the FID on the DOTA dataset decreased by 23.927 and the IS was improved by 2.351. The comparison experiments show that the method proposed in this paper can generate more realistic sample images, and images generated by this method can improve object detection metrics by increasing the number of single-category datasets and the number of targets in fewer categories in multi-category datasets, which means it can be effectively used in the field of intelligent processing of remote sensing images.
Coordinate Attention Filtering Depth-Feature Guide Cross-Modal Fusion RGB-Depth Salient Object Detection

Existing RGB + depth (RGB-D) salient object detection methods mainly focus on better integrating the cross-modal features of RGB images and depth maps. Many methods use the same feature interaction module to fuse RGB and depth maps, which ignores the inherent properties of different modalities. In contrast to previous methods, this paper proposes a novel RGB-D salient object detection method that uses a depth-feature guide cross-modal fusion module based on the properties of RGB and depth maps. First, a depth-feature guide cross-modal fusion module is designed using coordinate attention to utilize the simple data representation capability of depth maps effectively. Second, a dense decoder guidance module is proposed to recover the spatial details of salient objects. Furthermore, a context-aware content module is proposed to extract rich context information, which can predict multiple objects more completely. Experimental results on six benchmark public datasets demonstrate that, compared with 15 mainstream convolutional neural network detection methods, the saliency map edge contours detected by the proposed model have better continuity and the spatial structure details are clearer. Perfect results are achieved on four quantitative evaluation metrics. Furthermore, the effectiveness of the three proposed modules is verified through ablation experiments.
A new weighted extreme learning machine based on elastic net regularization embedded exponential regularized discriminative dictionary learning for image classification

Abstract It is well known that discriminative sparse representation can significantly improve the performance of image classification. However, there remain several tricky issues to be addressed due to the unsatisfied performance and high time consumption. In this paper, a novel classification framework called weighted extreme learning machine exponential regularized discriminative dictionary learning (WELM-ERDDL) is proposed to address these issues. The main contributions of this paper include (1) the WELM is embedded with ERDDL via exponential regularized linear discriminative analysis (ERLDA) for feature mappings while enabling nonlinear and diverse feature representation; (2) in the ELM learning process, the elastic net regularization is utilized to optimize more robust and meaningful output weights; (3) an effective weight update rule is designed for WELM. To verify the effectiveness of the proposed method, several experiments are conducted on real-world image classification databases. The results show that the proposed WELM-ERDDL framework is even more efficient than other state-of-the-art algorithms in general.
Data Symmetries and Learning in Fully Connected Neural Networks

Symmetries in the data and how they constrain the learned weights of modern deep networks is still an open problem. In this work we study the simple case of fully connected shallow non-linear neural networks and consider two types of symmetries: full dataset symmetries where the dataset <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> is mapped into itself by any transformation <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$g$ </tex-math></inline-formula> , i.e. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$gX=X$ </tex-math></inline-formula> or single data point symmetries where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$gx=x$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x\in X$ </tex-math></inline-formula> . We prove and experimentally confirm that symmetries in the data are directly inherited at the level of the network’s learned weights and relate these findings with the common practice of data augmentation in modern machine learning. Finally, we show how symmetry constraints have a profound impact on the spectrum of the learned weights, an aspect of the so-called network implicit bias.
Image Quality Assessment for Realistic Zoom Photos

New CMOS imaging sensor (CIS) techniques in smartphones have helped user-generated content dominate our lives over traditional DSLRs. However, tiny sensor sizes and fixed focal lengths also lead to more grainy details, especially for zoom photos. Moreover, multi-frame stacking and post-sharpening algorithms would produce zigzag textures and over-sharpened appearances, for which traditional image-quality metrics may over-estimate. To solve this problem, a real-world zoom photo database is first constructed in this paper, which includes 900 tele-photos from 20 different mobile sensors and ISPs. Then we propose a novel no-reference zoom quality metric which incorporates the traditional estimation of sharpness and the concept of image naturalness. More specifically, for the measurement of image sharpness, we are the first to combine the total energy of the predicted gradient image with the entropy of the residual term under the framework of free-energy theory. To further compensate for the influence of over-sharpening effect and other artifacts, a set of model parameters of mean subtracted contrast normalized (MSCN) coefficients are utilized as the natural statistics representatives. Finally, these two measures are combined linearly. Experimental results on the zoom photo database demonstrate that our quality metric can achieve SROCC and PLCC over 0.91, while the performance of single sharpness or naturalness index is around 0.85. Moreover, compared with the best tested general-purpose and sharpness models, our zoom metric outperforms them by 0.072 and 0.064 in SROCC, respectively.
Problems of designing fire protection systems for subways

В статье рассмотрены проблемы проектирования систем обеспечения противопожарной защиты метрополитенов. Отмечено, что вопросам обеспечения пожарной безопасности на объектах метрополитенов уделяется особое внимание со стороны как контрольно-надзорных органов, так и специалистов, участвующих в разработке нормативных документов. При недостаточном уровне защиты от пожара нахождение в метрополитене чрезвычайно опасно, поскольку существует удаленность от поверхности и возможность быстрого задымления, что может привести к массовой гибели людей и значительным социально-экономическим последствиям. Предложены пути решения проблем, связанных с обеспечением пожарной безопасности метрополитенов. The article deals with the problematic issues of designing systems for ensuring fire protection of subways. To date, there is no precise definition of the functional fire hazard class for a metro station in regulatory documents. An object with a massive stay of people, primarily in terms of evacuation of people in case of fire, is considered one of the most difficult. Based on this fact alone, when designing a metro line and stations, special technical conditions are necessarily developed. The existing set of rules for subways is not included in the list of regulatory documents that ensure compliance with fire safety requirements. Many questions arise when designing smoke ventilation systems, since the provisions of the existing set of rules, as one of the main regulatory documents for the design of smoke protection systems, cannot be fully applied to underground metro structures. The parameters and modes of air exchange in an extensive metro network depend not only on the selected operating modes of ventilation equipment, but also on the piston effect from the movement of trains in tunnels, on the configuration of buildings and wind load in the area of ventilation shafts on the surface, season, etc. A separate important issue is ensuring the stability of the air flow in case of fire. In practice, the phenomenon of “overturning the jet” is known – a change in direction, a reversal of the movement of the air flow in the mine due to a change in pressure in the area with a fire. According to the set of rules, the stability of the air flow is ensured if the calculated speed of the air flow in the area exceeds the critical speed. The critical speed in this case depends only on the section of the tunnel and the slope. Neither the design power of the fire, nor other significant parameters are taken into account when determining it, which, of course, is not entirely correct from a physical point of view. The ways of solving problematic issues related to ensuring the fire safety of subways are proposed.
The Effect of Parent Attention and Teacher's Competency on Students Learning Outcomes During Online Learning

Indonesian language learning content is a program to develop knowledge, language skills, and a positive attitude towards the Indonesian language itself as well as respect for humans and human values. The aims of the study were to analyze the relationship between parental attention and Indonesian language learning outcomes, the relationship between teacher competence and Indonesian learning outcomes, and the relationship between parental attention and teacher competence and Indonesian learning outcomes. This type of quantitative descriptive research. The research population was 344 students of class V elementary school. The research sample was 185 students with random sampling technique. Data collection techniques using questionnaires and documentation. Data analysis using descriptive statistics and inferential statistics. The results of the study showed that there was no positive and significant relationship between parental attention and the results of learning Indonesian, because the sig score of 0.152 was more than 0.05. There is a significant positive relationship between teacher competency and Indonesian language learning outcomes, because the sig 0.004 score is less than 0.05. There is a jointly significant positive relationship between parental attention and teacher competency with Indonesian language learning outcomes, because the sig or 0.013 result is less than 0.05. Based on the discussion and conclusions, several suggestions can be put forward, including that parents play a very important role in the child's learning process, especially in this case the Indonesian language subject. Therefore, parents must pay attention to their child's learning process.
On the Finiteness of Perfect Powers in Elliptic Divisibility Sequences

We prove that there are finitely many perfect powers in elliptic divisibility sequences generated by a non-integral point on elliptic curves of the form y 2 =x(x 2 +b), where b is any positive integer. We achieve this by using the modularity of elliptic curves over real quadratic number fields.
टिमुरको झोल कथासङ्ग्रहमा परिवेश चित्रण {Environmental Illustration in the Collection of Stories, Timurko Jhol}

प्रस्तुत लेख विश्वप्रेम अधिकारीको ‘टिमुरको झोल’ कथासङ्ग्रहमा समाविष्ट कथाहरूमा प्रयुक्त परिवेशको अध्ययनमा केन्द्रित छ । परिवेश चित्रण सम्बन्धी अवस्थाको अध्ययन यस अध्ययनको प्रमुख शोध्य समस्या हो । कथा सिर्जनामा परिवेशको विशेष महत्त्व रहन्छ । कथानकको विकास र पात्रहरूको गतिशीलताका साथै कथामा जीवन्तता, विश्वसनीयता, युगजनीनता जस्ता वैशिष्ट्य कायम गर्न परिवेशको अहम् भूमिका रहन्छ । तसर्थ यस शोधलेखमा परिवेश चित्रण र विश्लेषण सम्बन्धी नवीनतम सैद्धान्तिक धारणाका पक्षहरू काल, समय, अवस्थाहरूको अध्ययन सैद्धान्तिक रूपमा गर्नुका साथै ’टिमुरको झोल’ कथासङ्ग्रहमा केकस्तो परिवेश चित्रण पाइन्छ भन्ने समस्यामूलक प्रश्नमा केन्द्रित रहेर ‘टिमुरको झोल’ कथासङ्ग्रहमा समाविष्ट कथाहरूमा प्रयुक्त परिवेशलाई केन्द्रीयतामा राखी विश्लेषण कार्यलाई सम्पन्न गरी निष्कर्षमा पुर्याउने उद्देश्यमा यो शोध्यलेख केन्द्रित रहेको छ । ‘टिमुरको झोल’ सङ्ग्रहभित्रका कथाहरू प्रस्तुत लेखको मुख्य स्रोत सामग्री हो । पुस्तकालको प्रयोग गरी सङ्कलन गरिएका द्वितीय स्रोतका सैद्धान्तिक सामग्रीहरूलाई आधार बनाएर पहिचानात्मक तथा विश्लेषणात्मक विधिमा आधारित रहेर सम्पन्न गरिएको यो लेख परिवेश सम्बन्धी सैद्धान्तिक अवधारणाका साथै विश्वप्रेम अधिकारीका कथाहरूमा प्रयुक्त परिवेशको अध्ययनका दृष्टिले विशेष महत्त्वको रहेको छ । {The present article focuses on the study of the environment used in the stories included in Vishwaprem Adhikari's 'Timurko Jhol' collection. The study of the situation related to the depiction of the environment is the major research problem of this study. Circumstances are of special importance in storytelling. The environment plays an important role in the development of the story and the dynamics of the characters as well as in maintaining the vitality, reliability and epoch-making features in the story. Therefore, this study focuses on the theoretical aspects of the latest theoretical concepts related to environment depiction and analysis, focusing on the problematic question of what kind of environment depiction is found in the collection of stories 'Timurko Jhol'. The stories in the 'Timurko Jhol' collection are the main source material of the present article. Based on the theoretical material of the second source compiled using the library and based on the identification and analytical method, this article is of special importance from the point of view of the theoretical concept of environment as well as the study of the environment used in the stories of cosmopolitan officials.}