Reviewerly/exHarmony huggingface dataset:
Applying Quaternions to Recognize Hidden Details in Images: Rothko as a Case Study

Images or paintings with homogeneous colors may appear dull to the naked eye; however, there may be numerous details in the image that are expressed through subtle changes in color. This manuscript introduces a novel approach that can uncover these concealed details via a transformation that increases the distance between adjacent pixels, ultimately leading to a newly modified version of the input image. We chose the artworks of Mark Rothko—famous for their simplicity and limited color palette—as a case study. Our approach offers a different perspective, leading to the discovery of either accidental or deliberate clusters of colors. Our method is based on the quaternion ring, wherein a suitable multiplication can be used to boost the color difference between neighboring pixels, thereby unveiling new details in the image. The quality of the transformation between the original image and the resultant versions can be measured by the ratio between the number of connected components in the original image (m) and the number of connected components in the output versions (n), which usually satisfies nm≫1. Although this procedure has been employed as a case study for artworks, it can be applied to any type of image with a similar simplicity and limited color palette.
Effects of congruity on the state of user presence in virtual environments: Results from a breaching experiment

The present study investigates how the user state of presence is affected by contingencies in the design of virtual environments. The theoretical framework of congruity is herein explicated, which builds upon the concept of plausibility illusion as one of the essential prerequisites for presence, and which systematically explains and predicts presence in terms of alignment between schemata in the user’s memory and stimuli presented within the virtual environment. Three dimensions of congruity are explicated and discussed: sensory , environmental , and thematic . A series of breaching experiments were conducted in a virtual environment testing the effects of each dimension of incongruity on presence. These experiments were inconclusive regarding the effects of sensory and environmental congruity; however, the results strongly suggest that the state of presence is contingent upon thematic congruity in virtual environments. This finding has theoretical significance insofar as it points towards the necessity of considering genre and cultural context in predicting user states in virtual environments. The study also has practical relevance to designers and developers of content for virtual reality in that it identifies a critical psychological consideration for the user experience that is absent from existing models.
Overcoming the DDoS Attack Vulnerability of an ISO 19847 Shipboard Data Server

The maritime industry, which transports approximately 90% of the world’s goods, plays a crucial role in the global economy. However, increasing reliance on digital technologies has made the industry vulnerable to cybersecurity threats that may compromise the safety and security of maritime operations, thereby potentially affecting global supply chain integrity and public safety. This study examines the vulnerability of the ISO 19847:2018 standard shipboard data server to distributed denial-of-service (DDoS) attacks and proposes a method to mitigate this vulnerability. To this end, we propose modifications to the MQTT v5 protocol used by the shipboard data server, which provides streaming data-transfer services, and conduct verification experiments. These modifications allow the shipboard data server to control the frequency of messages published by the MQTT publisher, thereby preventing it from being overwhelmed by massive amounts of traffic in the event of a DDoS attack. Therefore, the proposed method can enhance the overall cybersecurity of the maritime sector by preventing the misuse of onboard MQTT publishers and reducing the impact of DDoS attacks.
Development and verification of a user-friendly software for German text simplification focused on patients with cerebral palsy

People with disabilities, especially those who have been diagnosed with cerebral palsy or other neurological diseases, are often not able to understand the text they are confronted with in their daily life. Important information, for example from the government, is manually translated by professionals into easy language suitable for these people, however, this method cannot be scaled-up to a large number of documents. We have developed a software that translates German text into a simpler version. The software, based on the easy language rules, shortens the text and replaces difficult words with synonyms. To maintain a correct grammar during the synonym replacement, the software recognizes the phrase containing the replaced word and conjugates the other words in this phrase, if needed. For the verification of the software developed, the grammatical correctness of the replacement was quantified by German native speakers and the text shortening was compared against the results of 12 healthy volunteer readers. The software provides an easy-to-use interface, which takes into account both the mental and physical difficulties of the impaired people. Therefore, one can assume that the software will be a helpful tool for the acquirement of text content for these people.
Optimality Conditions for the Principle of Trajectory Division

This paper considers the problem of controlling a mobile robot in the presence of circular obstacles. To solve this problem, it is proposed to use the previously suggested principle of dividing permissible trajectories into a sequence of rectilinear sections and arcs of circles that are the boundaries of circular obstacles. The conditions for the solution based on this principle of optimality are obtained.
Application of graph neural network and feature information enhancement in relation inference of sparse knowledge graph

At present, knowledge embedding methods are widely used in the field of knowledge graph (KG) reasoning, and have been successfully applied to those with large entities and relationships. However, in research and production environments, there are a large number of KGs with a small number of entities and relations, which are called sparse KGs. Limited by the performance of knowledge extraction methods or some other reasons (some common-sense information does not appear in the natural corpus), the relation between entities is often incomplete. To solve this problem, a method of the graph neural network and information enhancement is proposed. The improved method increases the mean reciprocal rank (MRR) and [email protected] by 1.6% and 1.7%, respectively, when the sparsity of the FB15K-237 dataset is 10%. When the sparsity is 50%, the evaluation indexes MRR and [email protected] are increased by 0.8% and 1.8%, respectively.
Confidence-Guided Planar-Recovering Multiview Stereo for Weakly Textured Plane of High-Resolution Image Scenes

Multiview stereo (MVS) achieves efficient 3D reconstruction on Lambertian surfaces and strongly textured regions. However, the reconstruction of weakly textured regions, especially planar surfaces in weakly textured regions, still faces significant challenges due to the fuzzy matching problem of photometric consistency. In this paper, we propose a multiview stereo for recovering planar surfaces guided by confidence calculations, resulting in the construction of large-scale 3D models for high-resolution image scenes. Specifically, a confidence calculation method is proposed to express the reliability degree of plane hypothesis. It consists of multiview consistency and patch consistency, which characterize global contextual information and local spatial variation, respectively. Based on the confidence of plane hypothesis, the proposed plane supplementation generates new reliable plane hypotheses. The new planes are embedded in the confidence-driven depth estimation. In addition, an adaptive depth fusion approach is proposed to allow regions with insufficient visibility to be effectively fused into the dense point clouds. The experimental results illustrate that the proposed method can lead to a 3D model with competitive completeness and high accuracy compared with state-of-the-art methods.
A Conscious Cross-Breed Recommendation Approach Confining Cold-Start in Electronic Commerce Systems

When a new customer enters the spectrum of the E-Commerce system, the informative records and dataset, such as about the new user, purchasing history and other browsing data become insufficient, resulting in the emergence of one serious issue such as a Cold start problem (CSP). Furthermore, when the interaction among the product items becomes limited, a new problem such as Sparsity arises to handle such problems in E-Commerce system, we have designed an extensive and hybridized methodological approach known as Cold start and sparsity aware hybridized recommendation system (CSSHRS), to reduce the Sparsity of dataset as well as to overcome the cold start problem in the recommendation framework. The proposed CSSHRS technique has been predicted by using the dataset of Last. FM, and Book-Crossing resulted in Mean absolute percentage error (MAPE) of 37%, recalls 0.07, precision 0.18, Normalized Discounted Cumulative Gain (NDCD) 0.61, and F-measure 0.1. This article proves the proposed CSSHRS technique as an effective and efficient hybrid of RS against the issue of data sparsity as well as CSP.
Graph Convolution Point Cloud Super-Resolution Network Based on Mixed Attention Mechanism

In recent years, point cloud super-resolution technology has emerged as a solution to generate a denser set of points from sparse and low-quality point clouds. Traditional point cloud super-resolution methods are often optimized based on constraints such as local surface smoothing; thus, these methods are difficult to be used for complex structures. To address this problem, we proposed a novel graph convolutional point cloud super-resolution network based on a mixed attention mechanism (GCN-MA). This network consisted of two main parts, i.e., feature extraction and point upsampling. For feature extraction, we designed an improved dense connection module that integrated an attention mechanism and graph convolution, enabling the network to make good use of both global and local features of the point cloud for the super-resolution task. For point upsampling, we adopted channel attention to suppress low-frequency information that had little impact on the up-sampling results. The experimental results demonstrated that the proposed method significantly improved the point cloud super-resolution performance of the network compared to other corresponding methods.
Micro-seismic events detection and its tectonic implications in Northeastern Hainan Province

In this paper, we combine the U-net-based phase picking method (PhaseNet) with Graphics Processing Unit-Based Match and Locate technology (GPU-M&amp;amp;L) and a deep-learning-based seismic signal de-noising method (DeepDenoiser) as a workflow for automatically extracting micro-seismic information from continuous raw seismic data. PhaseNet is first used to detect missed seismic phases by scanning through the 5-year continuous waveform data recorded at five broad-band stations in Hainan province. Then Rapid Earthquake Association and Location method (REAL), VELEST program (1-D inversion of velocities and hypocenter locating) and HypoDD (a double-difference locating method) are applied to associate seismic phases with events and to locate, respectively. This initially established catalogue can be served as the template for the following match-filter work. We choose events with a high signal-to-noise ratio (SNR) as templates and apply GPU-M&amp;amp;L to detect more small earthquakes which are difficult to pick by routine methods due to the low SNR. Then, a deep learning-based noise reduction technique named DeepDenoiser is applied to extract seismic signal from noise to provide a better picking of arrival time and then to improve the relocation effects. Finally, we use HypoDD to relocate these events with P- and S- wave arrival times picked by PhaseNet. Compared with the five events listed in the China Earthquake Networks Center routine catalogue, in this study, we detect and locate 977 earthquakes by following the above procedure. Our relocation results illustrate quite a complex distribution pattern of events due to the complicated fault system in the northeastern part of Hainan Province.
Predicting Facebook sentiments towards research

Social media platforms provide users with various ways of interacting with each other, such as commenting, reacting to posts, sharing content, and uploading pictures. Facebook is one of the most popular platforms, and its users frequently share and reshare posts, including research articles. Moreover, the reactions feature on Facebook allows users to express their feelings towards the content they view, providing valuable data for analysis. This study aims to predict the emotional impact of Facebook posts relating to research articles. We collected data on Facebook posts related to various scientific research domains, including Health Sciences, Social Sciences, Dentistry, Arts, and Humanities. We observed Facebook users’ reactions towards research articles and posts and found that ‘Like’ reactions were the most common. We also noticed that research articles from the Dentistry research domain received a lot of ‘Haha’ reactions. We used machine learning models to predict the sentiment of Facebook posts related to research articles. We used features such as the research article’s title sentiment, abstract sentiment, abstract length, author count, and research domain to build the models. We used five classifiers: Random Forest, Decision Tree, K-Nearest Neighbors, Logistic Regression, and Naïve Bayes. The models were evaluated using accuracy, precision, recall, and F-1 score metrics. The Random Forest classifier was the best model for two- and three-class labels, achieving accuracy measures of 86% and 66%, respectively. We also evaluated the feature importance for the Random Forest model and found that the sentiment of the research article’s title is crucial in predicting the sentiment of the Facebook post. This study has substantial implications for public engagement in science-related messages. The emotional reactions of Facebook users towards research articles and posts can provide valuable insights into public engagement in science, and predicting the emotional impact of Facebook posts related to research articles can help researchers understand how the public perceives scientific research. The findings of the study can aid researchers in effectively communicating their research and engaging the public in scientific discourse.
RMFNet: Redetection Multimodal Fusion Network for RGBT Tracking

The development of single-modality target tracking based on visible light has been limited in recent years because visual light images are highly susceptible to environmental and lighting influences. Thermal infrared images can well compensate for this defect, so RGBT tracking has attracted increasing attention. However, existing studies are limited to the aggregation of multimodal information using feature fusion, ignoring the role of decision-level fusion in tracking, and the original re-detection algorithm in the used baseline model is prone to the accumulation of failures. To deal with these problems, we propose the Redetection Multimodal Fusion Network (RMFNet). The network is divided into three branches, the visible light branch, the thermal infrared branch, and the fusion branch. The three-branch structure can plainly utilize the complementary advantages of multimodal information and the commonalities and specific characteristics of the two modalities. We propose a multimodal feature fusion module (EFM), which can adaptively calculate the reliability of the modality and perform a weighted fusion of the two-modality features features. The existing redetection algorithm is improved, and the re-detection mechanism of global search in the current frame is added to reduce the accumulation of failures. We have conducted extensive comparative validation on two widely used benchmark datasets, GTOT and RGBT234. The outcomes of the experiments suggest that RMFNet outperforms other tracking methods.
A novel density peaks clustering algorithm for automatic selection of clustering centers based on K-nearest neighbors

The density peak clustering algorithm (DPC) requires manual determination of cluster centers, and poor performance on complex datasets with varying densities or non-convexity. Hence, a novel density peak clustering algorithm is proposed for the automatic selection of clustering centers based on K-nearest neighbors (AKDPC). First, the AKDPC classifies samples according to their mutual K-nearest neighbor values into core and non-core points. Second, the AKDPC uses the average distance of K nearest neighbors of a sample as its density. The smaller the average distance is, the higher the density. Subsequently, it selects the highest density sample among all unclassified core points as a center of the new cluster, and the core points that satisfy the merging condition are added to the cluster until no core points satisfy the condition. Afterwards, the above steps are repeated to complete the clustering of all core points. Lastly, the AKDPC labels the unclassified non-core points similar to the nearest points that have been classified. In addition, to prove the validity of AKDPC, experiments on manual and real datasets are conducted. By comparing the AKDPC with classical clustering algorithms and excellent DPC-variants, this paper demonstrates that AKDPC presents higher accuracy.
Performance Analysis of Machine Learning Algorithms on Product Reviews

Digital reviews now have a significant impact on how consumers communicate globally and the effect of review on online purchases. Sentiment analysis is one of most common field for analysing and extracting information from data of text format from multiple platforms like Facebook, Amazon, Twitter, and others. This research aims to discuss and analyse the sentiments stated in Amazon product reviews using Support Vector Machine (SVM), Naive Bayes, Random Forest, and Decision Tree algorithms. The machine learning algorithms classify reviews as positive or negative. The performance of the algorithms is evaluated in terms of metrics such as accuracy, precision, recall and F score. As per the validated results, SVM performs better on accuracy rate compared to the other algorithms.
Research on the Construction of Vocal Music Teaching Mode based on Deep Learning Model

In order to improve the effect of sports strategy formulation, this paper combines artificial intelligence assisted agile Internet of Things to conduct research on sports simulation scheme, and builds an intelligent sports simulation system.The sports simulation model designed in this paper can change the mechanism parameters according to the actual situation of the moving object, establish the fixed coordinate system and local coordinate system of the parallel platform model, and establish the mathematical model of the parallel platform.Moreover, according to the principle of vector closure, the inverse kinematics position solution is analyzed, and the inverse kinematic velocity solution is derived by using the equation of the inverse position solution.The experimental research results show that the sports simulation system based on artificial intelligence-assisted agile Internet of Things proposed in this paper has a good performance in sports simulation.
Adaptive online variance estimation in particle filters: the ALVar estimator

Abstract We present a new approach—the estimator—to estimation of asymptotic variance in sequential Monte Carlo methods, or, particle filters. The method, which adjusts adaptively the lag of the estimator proposed in Olsson and Douc (Bernoulli 25(2):1504–1535) applies to very general distribution flows and particle filters, including auxiliary particle filters with adaptive resampling. The algorithm operates entirely online, in the sense that it is able to monitor the variance of the particle filter in real time and with, on the average, constant computational complexity and memory requirements per iteration. Crucially, it does not require the calibration of any algorithmic parameter. Estimating the variance only on the basis of the genealogy of the propagated particle cloud, without additional simulations, the routine requires only minor code additions to the underlying particle algorithm. Finally, we prove that the estimator is consistent for the true asymptotic variance as the number of particles tends to infinity and illustrate numerically its superiority to existing approaches.
Creepage Distance Estimation of Hairpin Stators Using 3D Feature Extraction

The increasing demand for electric drives challenges conventional powertrain designs and requires new technologies to increase production efficiency. Hairpin stator manufacturing technology enables full automation, and quality control within the process is particularly important for increasing the process capacity, avoiding rejects and for safety-related aspects. Due to the complex, free-form geometries of hairpin stators and the required short inspection times, inline reconstruction and accurate quantification of relevant features is of particular importance. In this study, we propose a novel method to estimate the creepage distance, a feature that is crucial regarding the safety standards of hairpin stators and that could be determined neither automatically nor accurately until now. The data acquisition is based on fringe projection profilometry and a robot positioning system for a highly complete surface reconstruction. After alignment, the wire pairs are density-based clustered so that computations can be parallelized for each cluster, and an analysis of partial geometries is enabled. In several further steps, stripping edges are segmented automatically using a novel approach of spatially asymmetric windowed local surface normal variation, and the creepage distances are subsequently estimated using a geodesic path algorithm. Finally, the approach is examined and discussed for an entire stator, and a methodology is presented that enables the identification of implausible estimated creepage distances.
How the Gaming Mouse Connects the Virtual World and the Body

Gaming mice have complicated designs for different online games.This study analyzes the following questions: How does the computer game type affect the mouse's shape?How can players exist in a game's physical and virtual world through their body movements while operating the gaming mouse?The results show that there are many interesting correlations between plays' gestures and game actions.The gaming mouse has a sophisticated design to better respond to players' body movements.Furthermore, players develop their own body strategies that respond to the mouse's shape and integrate them into their constructed imaginary world.Different visual elements and control devices in human-computer interfaces compensate for the "cybernetic loss" experienced during the dematerialization process.The application of such compensation extends from plane graphics to gaming mice, and ranges from visual perception to the physical experience of controlling the mouse.