Reviewerly/exHarmony huggingface dataset:
Virtual reality and tourism destinations marketing: can it transform travel? evaluating the impact of immersive experiences on travel intentions

Authors:
raw_author_name: Kiran Nair
raw_affiliation_string: Department of Business Management, Abu Dhabi University, Abu Dhabi, United Arab Emirates
raw_author_name: Jamshid Bakhtiyorovich Ergashev
raw_affiliation_string: Dean of the Faculty of Mathematics and Informatics, Jizzakh State Pedagogical, Jizzakh, Uzbekistan
raw_author_name: K. D. V. Prasad
raw_affiliation_string: Symbiosis Institute of Business Management, Hyderabad, India

Abstract: This study investigates the relationship between the immersive VR experience, destination cognitive image, destination affective image, and tourists’ travel intention. The sample of this research is 167 visitors of three tourist attractions of Isfahan Province in Iran, through 360-degree virtual reality videos. In order to collect research data, an online survey and convenience sampling methods were used. PLS-SEM was utilised to assess both the measurement and structural models. The findings indicated that among factors of external technical environment stimuli, media richness has a stronger effect on the formation of the destination's cognitive image than the affective image. Also, perceived interactivity compared to media richness has a significant impact on the formation of the destination's affective image. In addition, vision consistency as an internal technical environment stimulus has a positive effect on the formation of the cognitive and affective image of the destination. Finally, the affective image of the destination resulting from the immersive VR experience has a stronger effect on travel intention than the cognitive image.
A Lightweight Multi-Branch Context Network for Unsupervised Underwater Image Restoration

Authors:
raw_author_name: Yonghui Zhang
raw_affiliation_string: School of Information and Communication Engineering, Hainan University, Haikou 570228, China

Abstract: Underwater images commonly experience degradation caused by light absorption and scattering in water. Developing lightweight and efficient neural networks to restore degraded images is challenging because of the difficulty in obtaining high-quality paired images and the delicate trade-off between model performance and computational demands. To provide a lightweight and efficient solution for restoring images in terms of color, structure, texture details, etc., enabling the underwater image restoration task to be applied in real-world scenes, we propose an unsupervised lightweight multi-branch context network. Specifically, we design two lightweight multi-branch context subnetworks that enable multiple receptive field feature extraction and long-range dependency modeling to estimate scene radiance and transmission maps. Gaussian blur is adopted to approximate the global background light on the twice-downsampled degraded image. We design a comprehensive loss function that incorporates multiple components, including self-supervised consistency loss and reconstruction loss, to train the network using degraded images in an unsupervised learning manner. Experiments on several underwater image datasets demonstrate that our approach realizes good performance with very few model parameters (0.12 M), and is even comparable to state-of-the-art methods (up to 149 M) in color correction and contrast restoration.
A survey of feature matching methods

Authors:
raw_author_name: Qian Huang
raw_affiliation_string: Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China
raw_author_name: Xiaotong Guo
raw_affiliation_string: Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China
raw_author_name: Yiming Wang
raw_affiliation_string: Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China
raw_author_name: Huashan Sun
raw_affiliation_string: Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China
raw_author_name: Lijie Yang
raw_affiliation_string: Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China

Abstract: 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

Authors:
raw_author_name: Mariya Evtimova-Gardair
raw_affiliation_string: University Paris 1 Panthéon- Sorbonne, INRIA, Paris, FRANCE

Abstract: 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

Authors:
raw_author_name: Muhammad Atif
raw_affiliation_string: Department of Statistics, University of Peshawar, Peshawar, Pakistan
raw_author_name: Muhammad Farooq
raw_affiliation_string: Department of Statistics, University of Peshawar, Peshawar, Pakistan
raw_author_name: Muhammad Shafiq
raw_affiliation_string: Institute of Numerical Sciences, Kohat University of Science and Technology, Kohat, Pakistan

Abstract: 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.
STRATEGY IN SAMPRADAYA DISCOURSE ON FACEBOOK

Authors:
raw_author_name: I Wayan Numertayasa
raw_author_name: Anang Santoso
raw_author_name: Moch. Syahri

Abstract: 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

Authors:
raw_author_name: Seungeun Lee
raw_affiliation_string: Department of Applied and Computer Science and Engineering Major in Bio Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea
raw_author_name: Kyungtae Kang
raw_affiliation_string: Department of Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea
raw_author_name: Hyojoong Suh
raw_affiliation_string: School of Computer Science and Information Engineering, The Catholic University of Korea, Bucheon 14462, Republic of Korea

Abstract: 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

Authors:
raw_author_name: Ciaran Eising
raw_affiliation_string: Department of Electronic and Computer Engineering, University of Limerick, V94 T9PX Limerick, Ireland
raw_author_name: Martin Glavin
raw_affiliation_string: School of Engineering, University of Galway, H91 TK33 Galway, Ireland
raw_author_name: Edward Jones
raw_affiliation_string: School of Engineering, University of Galway, H91 TK33 Galway, Ireland

Abstract: 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.
Composite Backbone Small Object Detection Based on Context and Multi-Scale Information with Attention Mechanism

Authors:
raw_author_name: Xuesong Liu
raw_affiliation_string: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
raw_author_name: Baolin Liu
raw_affiliation_string: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China

Abstract: 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.
Stability assessment of forestry plant power system based on improved long short-term memory network

Authors:
raw_author_name: Liang Zhang
raw_affiliation_string: Beijing Kedong Electric Power Control System Co., Ltd. Beijing, 100000, China
raw_author_name: Guohui Tian
raw_affiliation_string: Beijing Kedong Electric Power Control System Co., Ltd. Beijing, 100000, China

Abstract: 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

Authors:
raw_author_name: Ruslan R. Gabdullin
raw_affiliation_string: omonosov Moscow State University
raw_author_name: Maksim D. Kazurov
raw_affiliation_string: Lomonosov Moscow State University
raw_author_name: Aleksey Yu. Puzik
raw_affiliation_string: Perm State National Research University
raw_author_name: Sofya I. Merenkova
raw_affiliation_string: Shirshov Institute of Oceanology of the Russian Academy of Sciences; Lomonosov Moscow State University,
raw_author_name: Natalya V. Badulina
raw_affiliation_string: Lomonosov Moscow State University
raw_author_name: Aleksey V. Ivanov
raw_affiliation_string: Institute of Geography RAS; Lomonosov Moscow State University, Tambov State Technical University
raw_author_name: Iskander R. Migranov
raw_affiliation_string: Lomonosov Moscow State University

Abstract: 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

Authors:
raw_author_name: Midori Tanaka
raw_affiliation_string: Graduate School of Global and Transdisciplinary Studies, Chiba University, Yayoi-cho 1-33, Inage-ku, Chiba 263-8522, Japan; Graduate School of Science and Engineering, Chiba University, Yayoi-cho 1-33, Inage-ku, Chiba 263-8522, Japan
raw_author_name: Takahiko Horiuchi
raw_affiliation_string: Graduate School of Science and Engineering, Chiba University, Yayoi-cho 1-33, Inage-ku, Chiba 263-8522, Japan

Abstract: 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.
Outdoor activity classification using smartphone based inertial sensor measurements

Authors:
raw_author_name: Saaveethya Sivakumar
raw_affiliation_string: Department of Electrical and Computer Engineering, Curtin University, Miri, 98009, Sarawak, Malaysia
raw_author_name: Gopal Sakarkar
raw_affiliation_string: Department of Computer Science and Applications, Dr Vishwanath Karad MIT World Peace University, Pune, 411038, Maharashtra, India
raw_author_name: Filbert H. Juwono
raw_affiliation_string: Department of Electrical and Electronic Engineering, Xi’an Jiaotong - Liverpool University, 215123, Suzhou, China
raw_author_name: Catur Apriono
raw_affiliation_string: Department of Electrical Engineering, Universitas Indonesia, Depok City, 16424, West Java, Indonesia

Abstract: Abstract Human Activity Recognition (HAR) deals with the automatic recognition of physical activities and plays a crucial role in healthcare and sports where wearable sensors and intelligent computational techniques are used. We propose a HAR algorithm that uses the smartphones accelerometer data for human activity recognition. In particular, we present a recurrent convolutional neural network-based HAR algorithm that combines a Convolutional Neural Network (CNN) to extract temporal features from the sensor data, a Fuzzy C-Means (FCM) clustering algorithm to cluster the features extracted by the CNN, and a Long Short-Term Memory (LSTM) network to learn the temporal dependencies between the features. We evaluate the proposed methodology on two distinct datasets: the MotionSense dataset and the WISDM dataset. We evaluate the proposed CNN-FCM-LSTM model on the publicly available MotionSense dataset to classify ten activity types: 1) walking upstairs, 2) walking downstairs, 3) jogging, 4) sitting, 5) standing, 6) level ground walking, 7) jumping jacks, 8) brushing teeth, 9) writing, and 10) eating. Next, we evaluate the model’s performance on the WISDM dataset to assess its ability to generalize to unseen data. On the MotionSense test dataset, CNN-FCM-LSTM achieves a classification accuracy of 99.69%, a sensitivity of 99.62%, a specificity of 99.63%, and a false positive rate per hour (FPR/h) of 0.37%. Meanwhile, it achieves a classification accuracy of 97.27% on the WISDM dataset. The CNN-FCM-LSTM model’s capability to classify a diverse range of activities within a single architecture is noteworthy. The results suggest that the proposed CNN-FCM-LSTM model using smartphone inputs is more accurate, reliable, and robust in detecting and classifying activities than the state-of-the-art models. It should be noted that activity recognition technology has the potential to aid in studying the underpinnings of physical activity, designing more effective training regimens, and simulating the rigors of competition in sports.