Reviewerly/exHarmony huggingface dataset:
A survey of feature matching methods

Abstract Feature matching plays a crucial role in computer vision, with applications in visual localization, simultaneous localization and mapping (SLAM), image stitching, and more. It establishes correspondences between sets of feature points from multiple images, enabling various tasks. Over the years, feature matching has witnessed significant development, with an increasing number of methods being applied. However, different methods exhibit different degrees of applicability in different scenarios and requirements due to their different rationales. To cope with these issues, a comprehensive analysis and comparison of matching methods are essential. Existing reviews often lack coverage of deep learning models and focus more on feature detection and description, neglecting the matching process. This survey investigates feature detection, description, and matching techniques within the feature‐based image‐matching pipeline. Representative methods, their mechanisms, and application scenarios are also briefly introduced. In addition, comprehensive evaluations of classical and state‐of‐the‐art methods are conducted through extensive experiments on representative datasets. Particularly, matching‐based applications are compared to fully demonstrate the advantages of the methods. Lastly, this survey highlights current problems and development directions in matching methods, serving as a reference for researchers in the field.
Data Mining from Knowledge Cases of COVID-19

A lot of articles were produced during the pandemic of COVID-19 and continue to be produced. The article proposes a system for diagnosis of COVID-19 disease. Also nowadays, the presentation of knowledge and the research for the reasoning algorithms are progressively improving in the domain of Artificial Intelligence. Besides these, distributed reasoning as a part of data mining has become a solution for the increasing everyday data amount. As a result, the paper proposes a case-based non-monotonic reasoner for uncertain and vague COVID-19 information that is appropriate for work with Big Data. Also, a COVID-19 knowledge base model is proposed. The reasoner implements rules for the distribution of the information that gives the possibility to work with Big data. The proposed reasoning algorithm is applied for COVID-19. It shows the implementation of the reasoner into the data mining system and the returned results from the system are evaluated. The results show that the system returns relatively high results concerning the other system for recommendation.
The least sample size essential for detecting changes in clustering solutions of streaming datasets

The clustering analysis approach treats multivariate data tuples as objects and groups them into clusters based on their similarities or dissimilarities within the dataset. However, in modern world, a significant volume of data is continuously generated from diverse sources over time. In these dynamic scenarios, the data is not static but continually evolves. Consequently, the interesting patterns and inherent subgroups within the datasets also change and develop over time. The researchers have paid special attention to monitoring changes in cluster solutions of evolving streams. For this matter, several algorithms have been proposed in the literature. However, to date, no study has examined the effect of variability in cluster sizes on the evolution of cluster solutions. Moreover, no guidance is available on determining the impact of cluster sizes on the type of changes they experience in the streams. In the present simulation study using artificial datasets, the evolution of clusters is examined concerning the variability in cluster sizes. The findings are substantial because tracing and monitoring the changes in clustering solutions have a wide range of applications in every field of research. This study determines the minimum sample size required in the clustering of time-stamped datasets.
Error Analysis and Condition Estimation of the Pyramidal Form of the Lucas-Kanade Method in Optical Flow

Optical flow is the apparent motion of the brightness patterns in an image. The pyramidal form of the Lucas-Kanade (LK) method is frequently used for its computation but experiments have shown that the method has deficiencies. Problems arise because of numerical issues in the least squares (LS) problem minAx−b22, A∈Rm×2 and m≫2, which must be solved many times. Numerical properties of the solution x0=A†b = (ATA)−1ATb of the LS problem are considered and it is shown that the property m≫2 has implications for the error and stability of x0. In particular, it can be assumed that b has components that lie in the column space (range) R(A) of A, and the space that is orthogonal to R(A), from which it follows that the upper bound of the condition number of x0 is inversely proportional to cosθ, where θ is the angle between b and its component that lies in R(A). It is shown that the maximum values of this condition number, other condition numbers and the errors in the solutions of the LS problems increase as the pyramid is descended from the top level (coarsest image) to the base (finest image), such that the optical flow computed at the base of the pyramid may be computationally unreliable. The extension of these results to the problem of total least squares is addressed by considering the stability of the optical flow vectors when there are errors in A and b. Examples of the computation of the optical flow demonstrate the theoretical results, and the implications of these results for extended forms of the LK method are discussed.
STRATEGY IN SAMPRADAYA DISCOURSE ON FACEBOOK

Background: The term sampradaya is used to denote the Hindu spiritual community in Bali with new traditions and ways originating in India starting in 1980. The debate about sampradaya also took place on social media, resulting in contesting discourses. Research on sampradaya discourse on social media is still very poorly conducted. Based on this, the study aims to determine the strategy of sampradaya discourse in the battle of discourse about sampradaya on Facebook. Methods: This research uses a combination of critical di methods Teun Van Dijk model and netnographic methods. Research data in the form of discourse was collected by copying communication between participants in the Facebook community environment. Result: Based on research, the strategies used by discourse in combat are symbolic capital strategies, reproduction strategies, distinctive strategies, rejection and exclusion strategies, and transformation strategies. Conclussion: The strategies employed in the discourse battles of the Sampradaya on Facebook encompass symbolic capital, reproduction, distinction, rejection and exclusion, and transformation strategies. The use of these strategies in discourse battles aims to maintain and, in some cases, alter the distribution of symbolic resources in relation to power hierarchies, thereby influencing the online community.
Lightweight Machine Learning Method for Real-Time Espresso Analysis

Coffee crema plays a crucial role in assessing the quality of espresso. In recent years, in response to the rising labor costs, aging population, remote security/authentication needs, civic awareness, and the growing preference for non-face-to-face interactions, robot cafes have emerged. While some people seek sentiment and premium coffee, there are also many who desire quick and affordable options. To align with the trends of this era, there is a need for lightweight artificial intelligence algorithms for easy and quick decision making, as well as monitoring the extraction process in these automated cafes. However, the application of these technologies to actual coffee machines has been limited. In this study, we propose an innovative real-time coffee crema control system that integrates lightweight machine learning algorithms. We employ the GrabCut algorithm to segment the crema region from the rest of the image and use a clustering algorithm to determine the optimal brewing conditions for each cup of espresso based on the characteristics of the crema extracted. Our results demonstrate that our approach can accurately analyze coffee crema in real time. This research proposes a promising direction by leveraging computer vision and machine learning technologies to enhance the efficiency and consistency of coffee brewing. Such an approach enables the prediction of component replacement timing in coffee machines, such as the replacement of water filters, and provides administrators with Before Service. This could lead to the development of fully automated artificial intelligence coffee making systems in the future.
An Efficient and Effective Image Decolorization Algorithm Based on Cumulative Distribution Function

Image decolorization is an image pre-processing step which is widely used in image analysis, computer vision, and printing applications. The most commonly used methods give each color channel (e.g., the R component in RGB format, or the Y component of an image in CIE-XYZ format) a constant weight without considering image content. This approach is simple and fast, but it may cause significant information loss when images contain too many isoluminant colors. In this paper, we propose a new method which is not only efficient, but also can preserve a higher level of image contrast and detail than the traditional methods. It uses the information from the cumulative distribution function (CDF) of the information in each color channel to compute a weight for each pixel in each color channel. Then, these weights are used to combine the three color channels (red, green, and blue) to obtain the final grayscale value. The algorithm works in RGB color space directly without any color conversion. In order to evaluate the proposed algorithm objectively, two new metrics are also developed. Experimental results show that the proposed algorithm can run as efficiently as the traditional methods and obtain the best overall performance across four different metrics.
Effects of Non-Compliance with the Protocol on InBody 770 in Students of Different Training

The research aimed to investigate whether alterations in the measurement protocol, accounting for dehydration, resulted in variations in body composition parameters among participants with different weekly training frequencies. The sample consited of ninety healthy participants categorized into three subgroups; the first group (n=28, body height 172.13±9.12 cm, body weight 67.05±11.38 kg) included students with low levels of weekly PA, the second group (n=35, body height 172.93±7.76 cm, body weight 67.53±9.06 kg) consisted of students whith medium level of PA, and the third group (n=27, body height 175.87±9.27 cm, body weight 71.14±11.58 kg) comprised students with high weekly training frequencies. Using the body composition analyzer, InBody770, various morphological characteristics were measured, including Body Height (BH), Body Weight (BW), Body Mass Index (BMI), Percent Body Fat (PBF%), Body Fat Mass (BFMkg), Fat Free Mass (FFM), Total Body Water (TBW), Intracellular Water (ICW), Extracellular Water (ECW), Proteins (PROT), Minerals (MNRL), Soft Lean Mass (SLM), Skeletal Muscle Mass (SMM), Waist-Hip Ratio (WHR), Visceral Fat Level (VFL), Visceral Fat Area (VFA), and Obesity Degree (OD). It can be detected that there is generally no significant difference between the initial and final measurements within the variables describing body composition. Additionally, when analyzing the effect size on the overall sample, it was found to be insignificant in almost all variables, except for the following parameters: Body Fat Mass (ES=.28); Body Mass Index (ES=.21); Percent Body Fat (ES=.21); Visceral Fat Level (ES=.24); Visceral Fat Area (ES=.26); Obesity Degree (ES=.22). The results of this study, following the water intake treatment, revealed a notable overall difference in body composition parameters. However, upon closer examination by group, it becomes evident that a statistically significant difference is particularly pronounced in individuals with a high exercise frequency (Group III), indicating their body’s efficient capacity for rapid water absorption into various body composition parameters. These findings underscore the critical importance of adhering to the prescribed protocol when diagnosing body composition using the InBody 770 device, particularly among highly trained individuals.
Service robot acceptance in museums: an empirical study using the service robot acceptance model (sRAM)

Purpose This study aims to apply the service robot acceptance model (sRAM) to examine how attitude toward human–robot interaction (HRI) and engagement influence consumer acceptance of service robots in a frontline setting. Design/methodology/approach Data was collected from 255 visitors who interacted with a robotic tour guide at a city museum. The data was analyzed using smart PLS 4.0. Findings The findings show the positive effects of subjective norms, appearance, perceived trust and positive emotion on both attitude toward HRI and engagement. In addition, social capability impacted attitude toward HRI, whereas perceived usefulness affected engagement. Practical implications To deliver engaging museum experiences that bring about positive word-of-mouth and intention to visit, managers need to incorporate the sRAM dimensions in the design and deployment of service robots. Originality/value This research uses field data to empirically validate the sRAM in the context of service robot acceptance. It introduces engagement as a novel mediating variable, enriching current understanding of human-like qualities in HRIs.
Teaching Software Quality Assurance (SQA) During COVID-19 Using the HyFlex Approach -- Course Design, Results, and Experiences

Abstract Teaching Software Quality Assurance (SQA) During COVID-19 using the HyFlex Approach -- Course Design, Results, and Experiences Abstract This full paper discusses the HyFlex pedagogical approach to teaching a highly interactive face-to-face Software Quality Assurance (SQA) course during the COVID-19 pandemic. HyFlex, short for hybrid-flexibility, is a teaching model where instructors and students have the option to attend scheduled courses face-to-face (F2F) or remotely. In this teaching model the learning outcomes for the course remain the same for all who enroll irrespective of the mode of attendance. This poses significant challenges for the instructor, as the learning outcomes have to be delivered in different modalities, but with the same quality. This is particularly difficult in SQA courses, as these require instruction in formal methods as well as systematic justification of engineering choices, both of which are best facilitated in a face-to-face fashion. Our HyFlex SQA approach consists of lectures (in class, with video recordings), face-to-face activities, group assignments (blackboard), group projects (blackboard), and exams (blackboard). During the lecture period, the instructor delivers content in the form of lecture slides and writing on a whiteboard. Think-Pair-Share (TPS) is an active learning technique in which students are allocated adequate time to think individually on a task posed by the instructor, followed by pair discussion and then as a class discussion. The task given by the instructor is of similar complexity to that which is covered as an example during lecture. Face-to-face activities involve students participating in TPS by working collaboratively as a group on tasks that correspond with concepts covered in the PowerPoint lecture. This way students learn from each other by thinking individually then sharing ideas in the classroom thus contributing to better understanding of course content. For assignments and projects students are allocated a portion of the class time to meet with group members and discuss their activities. Groups also have the option to ask the instructor questions aloud that will help other groups to complete their assignments and projects successfully. Delivering these in a hybrid format was required during the fall of 2020 due to COVID-19 restrictions. The results show an average, but not significant, decrease in students’ performance during fall 2020. We also noted a lower enrollment, higher participatory effort on both instructors and students, and a subjectively decreased feeling of collaboration. In this paper we adopted a HyFlex teaching model that incorporates reduced F2F seating, educational tools such as blackboard, blackboard collaboration, panopto, google docs, google meet, and zoom. We conclude by discussing some challenges experienced with HyFlex teaching model and recommendations for adopting the teaching model by other instructors who teach CS courses that involve a considerable amount of group activities.
Composite Backbone Small Object Detection Based on Context and Multi-Scale Information with Attention Mechanism

Object detection has gained widespread application across various domains; nevertheless, small object detection still presents numerous challenges due to the inherent limitations of small objects, such as their limited resolution and susceptibility to interference from neighboring elements. To improve detection accuracy of small objects, this study presents a novel method that integrates context information, attention mechanism, and multi-scale information. First, to realize feature augmentation, a composite backbone network is employed which can jointly extract object features. On this basis, to efficiently incorporate context information and focus on key features, the composite dilated convolution and attention module (CDAM) is designed, consisting of a composite dilated convolution module (CDM) and convolutional block attention module (CBAM). Then, a feature elimination module (FEM) is introduced to reduce the feature proportion of medium and large objects on feature layers; the impact of neighboring objects on small object detection can thereby be mitigated. Experiments conducted on MS COCO validate the superior performance of the method compared with baseline detectors, while it yields an average enhancement of 0.8% in overall detection accuracy, with a notable enhancement of 2.7% in small object detection.
Collaborative Encoding Method for Scene Text Recognition in Low Linguistic Resources: The Uyghur Language Case Study

Current research on scene text recognition primarily focuses on languages with abundant linguistic resources, such as English and Chinese. In contrast, there is relatively limited research dedicated to low-resource languages. Advanced methods for scene text recognition often employ Transformer-based architectures. However, the performance of Transformer architectures is suboptimal when dealing with low-resource datasets. This paper proposes a Collaborative Encoding Method for Scene Text Recognition in the low-resource Uyghur language. The encoding framework comprises three main modules: the Filter module, the Dual-Branch Feature Extraction module, and the Dynamic Fusion module. The Filter module, consisting of a series of upsampling and downsampling operations, performs coarse-grained filtering on input images to reduce the impact of scene noise on the model, thereby obtaining more accurate feature information. The Dual-Branch Feature Extraction module adopts a parallel structure combining Transformer encoding and Convolutional Neural Network (CNN) encoding to capture local and global information. The Dynamic Fusion module employs an attention mechanism to dynamically merge the feature information obtained from the Transformer and CNN branches. To address the scarcity of real data for natural scene Uyghur text recognition, this paper conducted two rounds of data augmentation on a dataset of 7267 real images, resulting in 254,345 and 3,052,140 scene images, respectively. This process partially mitigated the issue of insufficient Uyghur language data, making low-resource scene text recognition research feasible. Experimental results demonstrate that the proposed collaborative encoding approach achieves outstanding performance. Compared to baseline methods, our collaborative encoding approach improves accuracy by 14.1%.
Stability assessment of forestry plant power system based on improved long short-term memory network

Abstract The stability assessment of forestry power system plays a vital role in many aspects. First of all, the stability of the power system is directly related to the production efficiency, employee safety and environmental sustainability of the factory. If the power system is unstable, it may cause power outage or power fluctuations, which will interrupt the production and turn the production line, which will lead to a reduction in production capacity and the increase in production costs. In addition, the stability of the power system is directly related to the security of employees. Sudden power failure or voltage fluctuations may cause improper equipment operation, and may even cause accidents and damage. Therefore, by evaluating the stability of the power system, potential security risks can be reduced. In addition, unstable power systems may cause waste and increase environmental burden. In order to improve the stability of the power system, this article proposes an improved LSTM (long and short memory) model of improved stability prediction. This model uses the power system to operate data, including current, voltage, frequency, and other parameters, and external factors related to weather conditions and load changes to predict the stability of the power system. Compared with traditional methods, this method performs well in predicting the failure and abnormalities of the power system, and has the ability to evaluate high accuracy and stability. Therefore, the improved LSTM model method can be regarded as a powerful tool for power system management and maintenance.
HIGH-PRECISION CYCLIC CORRELATION AS A BASIS FOR DETAILED PALEOCLIMATIC RECONSTRUCTIONS FOR THE CRIMEAN-CAUCASIAN REGION IN JURASSIC TIME

An astrochronological (cyclostratigraphic) binding of the lithological and geochemical characteristics of the Jurassic deposits of the Mountainous Crimea and the Caucasus to the cycles of eccentricity of the Earth’s orbit is carried out, an analysis of the relationship between the established variations of climate and paleotemperature with the moments of coincidence of different-order cycles of eccentricity and their paleoclimatic characteristics is given. New data on paleotemperatures of the Southern Demerdzhi mountain section are presented.
Automatic MTF Conversion between Different Characteristics Caused by Imaging Devices

Depending on various design conditions, including optics and circuit design, the image-forming characteristics of the modulated transfer function (MTF), which affect the spatial resolution of a digital image, may vary among image channels within or between imaging devices. In this study, we propose a method for automatically converting the MTF to the target MTF, focusing on adjusting the MTF characteristics that affect the signals of different image channels within and between different image devices. The experimental results of MTF conversion using the proposed method for multiple image channels with different MTF characteristics indicated that the proposed method could produce sharper images by moving the source MTF of each channel closer to a target MTF with a higher MTF value. This study is expected to contribute to technological advancements in various imaging devices as follows: (1) Even if the imaging characteristics of the hardware are unknown, the MTF can be converted to the target MTF using the image after it is captured. (2) As any MTF can be converted into a target, image simulation for conversion to a different MTF is possible. (3) It is possible to generate high-definition images, thereby meeting the requirements of various industrial and research fields in which high-definition images are required.