Reviewerly/exHarmony huggingface dataset:
Dual efficient reversible data hiding using Hamming code and OPAP

Dual image based RDH is a separate research area of the traditional RDH method, which has been continuously studied by several researchers over the past decade. In the research of dual RDH, extension of the traditional RDH algorithm or methods of improving the existing DH algorithm to dual RDH have been proposed. Hamming code (HC) is a method used as a DH method, but it is not applied to RDH. In this paper, we propose a dual RDH method using Hamming codes. Previously, Jana et al. proposed HC(7,4)-based dual RDH, but in terms of performance, it showed a weakness that did not reach the performance of the previously proposed RDH. In this paper, we propose a dual RDH scheme using Hamming code and OPAP. As previously suggested by Jana et al. The performance of dual RDH of HC(7,4) has been greatly improved. In this paper, HC(3,1) is used to significantly improve data hiding capacity and image quality, i.e., since this method conceals 2-bit secret data in 3 pixels using the OPAP method and the Hamming code, the embedding rate is 1.0 bpp and the average of the two marked images is measured as 51 dB. With the efficiency of the proposed coding method, it is possible to maintain high image quality. As a result of the experiment, our proposed data hiding capacity greatly outperformed the method proposed by Jana et al., and compared to the existing methods, it was almost similar in capacity and showed good performance in terms of image quality.
Feature-based modeling for variable fractal geometry design integrated into CAD system

Fractal geometry has been widely applied in computer graphics to visually represent natural objects, which cannot be easily represented by Euclidean geometry. Other than visual representation, parametric fractal-like CAD models have been used in industry for the design and manufacture of products with self-similarity geometric features. However, contemporary CAD systems cannot construct CAD fractal models efficiently, as most CAD fractal models are still built manually by designers. The construction and modifications for such CAD fractal models are tedious and time-consuming. This paper aims to build and modify CAD fractal models more efficiently in CAD systems. A method for automatically modeling parametric fractal-like geometry objects in CAD systems is proposed. Feature-based modeling methods have been applied for realizing algorithm-based CAD fractal model generation. A new type of feature, called CAD fractal feature is proposed and implemented to bridge the gap between CAD and fractal geometry. The fractal-like CAD model is constructed iteratively with a dynamic generator for each iteration cycle. Design parameters are extracted as inputs for dynamic fractal generators, which allows users to define, parameterize, generate and edit a fractal design with variable parameters. The parameters of the fractal generator can also take different values for different model construction iterations, which makes the generated models not necessarily to be strictly fractal but can also be fractal-like. The iterative generation method (IGM) is proposed as a built-in method in a CAD fractal feature for modeling complex but self-similar geometric models in an objected-oriented approach with high efficiency and sufficient design flexibility. As a result, designers do not need advanced knowledge of fractal geometry to build and modify CAD models with self-similar features. An extruded Koch Snowflake CAD model with parametric geometric variants has been built as a case study for demonstration of the proposed modeling method.
CREATIVITY AS THE MOST IMPORTANT COMPONENT OF PERSONALITY DEVELOPMENT AND SELF-REALIZATION

The main feature of creativity is the creation of new values of material and spiritual culture. Modern scientists, when exploring creativity, use two basic approaches, one of which views creativity as an activity that creates a new product that has social value, and the second approach defines creativity as a means of self-realization of man. The self-development of man has caused the attention of the philosophers of the ancient world. The development of both physical and spiritual, intellectual abilities of man were interested in ancient philosophers, such as Democritus, Socrates, Plato, Aristotle, who believed that self-development was the main purpose of human life.Creativity in antiquity is considered in the aspect of the divine, as the creation of cosmos and human-craft, art, where creativity is embodied in subject-practical activity. Thomas Aquinas, Aurelius Augustine, Erasmus Rotterdam, J. Bruno, B. Spinoza linked self-development with the need to improve human spiritual forces. The Renaissance embodies the idea that man is a master and sculptor himself, and knowledge is an act of individual creativity. For representatives of German classical philosophy G. Hegel, I. Kant, F. Schelling Self-Development is a freedom of choice. In N. Berdyaev, creativity is a higher manifestation of the spiritual essence of man. G. Skovoroda, because of the principle of “affinity”, considers the conditions for the development of human creative abilities. Undoubtedly, the center of creativity is the personality itself, without which creative activity is impossible. Personality has knowledge and skills, abilities, motivation through which it has the opportunity to create a new, unique product. And in order to reveal and expand creative opportunities, personality, it is necessary to have a developed imagination, intuition, creative perception, creative thinking and motivation for the activity in which it can self-actualize itself. Self-development, self-realization of personality, integrity of the components of its individuality is a consequence and condition of creative activity, and the development of socially significant values contributes to improving the level of social maturity of the individual. Therefore, we can talk about the interdependence of creative activity and self-realization of personality in society.
Emphasizing feature inter-class separability for improving highly imbalanced overlapped data classification

Class imbalance hinders the performance of some standard classifiers. However, class imbalance may not be solely responsible for the decrease in performance. Research efforts show that imbalanced datasets suffer from overlapping problems and borderline samples, which often deteriorate the classification performance. Conventional imbalanced learning methods mainly focus on balancing the distribution between classes but ignore difficulties caused by the above problems, thus underperforming drastically. This paper proposes a hybrid network called SemiPro-Empha to alleviate the aforementioned problems by learning a feature space with good inter-class separability and intra-class compactness. SemiPro-Empha comprises two modules: a feature learning loss called Semi-Prototype contrastive loss (Semi-Proto), guiding the feature extractor to learn a feature space where the projections of original overlapping classes can be separated, thereby improving classification performance. Additionally, this paper also presents a robust valuable borderline sample mining strategy called Emphasizing (Empha). Emphasizing identifies "valuable" borderline samples and eliminates noisy samples to create an auxiliary training dataset during each training epoch, providing up-to-date global classification boundary information for the training model while ensuring its robustness. Extensive experiments conducted on the breast cancer dataset and seven imbalanced datasets demonstrate the effectiveness of SemiPro-Empha.
The Impact of Behaviorist Theories and the Legal System Laws Developed from Them

The theories, which stress how environmental cues act as catalysts for criminal action, are based on ideas of imitation and learning. To comprehend criminal behavior as a learned or copied reaction to societal and environmental stimuli, this research emphasizes the significance of psychological deviations. The macro and micro layers of crimes are the main topics of this study’s investigation of psychological theories of the criminal justice system. In order to understand how societal and environmental factors affect individual criminal conduct, sociological and psychological perspectives on crime are used in this analysis. The idea of a mentor or guide is introduced, making the argument that people frequently need assistance and direction to develop their personalities. This research concludes that in the 20th century, blatant crimes such as drug trafficking and gambling undermined the rights of others to life and permanently severed the person from the mental conscious level, transferring him to an unconscious, unreal, obscured, and incomprehensible reality. This article reveals that men struggle more than women to communicate and comprehend their emotions, which makes them more likely to be charged with crimes.
Visualizing ordered bivariate data on node-link diagrams

Node-link visual representation is a widely used tool that allows decision-makers to see details about a network through the appropriate choice of visual metaphor. However, existing visualization methods are not always effective and efficient in representing bivariate graph-based data. This study proposes a novel node-link visual model — visual entropy (Vizent) graph — to effectively represent both primary and secondary values, such as uncertainty, on the edges simultaneously. We performed two user studies to demonstrate the efficiency and effectiveness of our approach in the context of static node-link diagrams. In the first experiment, we evaluated the performance of the Vizent design to determine if it performed equally well or better than existing alternatives in terms of response time and accuracy. Three static visual encodings that use two visual cues were selected from the literature for comparison: Width-Lightness, Saturation-Transparency, and Numerical values. We compared the Vizent design to the selected visual encodings on various graphs ranging in complexity from 5 to 25 edges for three different tasks. The participants achieved higher accuracy of their responses using Vizent and Numerical values; however, both Width-Lightness and Saturation-Transparency did not show equal performance for all tasks. Our results suggest that increasing graph size has no impact on Vizent in terms of response time and accuracy. The performance of the Vizent graph was then compared to the Numerical values visualization. The Wilcoxon signed-rank test revealed that mean response time in seconds was significantly less when the Vizent graphs were presented, while no significant difference in accuracy was found. The results from the experiments are encouraging and we believe justify using the Vizent graph as a good alternative to traditional methods for representing bivariate data in the context of node-link diagrams.
Non-contact type dynamic responses test of wind turbines

The performance analysis of wind turbine systems should be considered when calculating the wind speed relative to the wind turbine structure, and is essential for wind turbine design. Since the conditions are precarious in transient state and the operating environments are challenging, the wind turbine is a complex, multivariate, nonlinear system. This paper presents a novel dynamic response test method for wind turbines based on three-dimensional digital speckle measurement. This method use a real-time speckle image collection of objects in various stages using binocular stereo vision to perform stereo matching of deformation points on object surface. A digital image correlation algorithm is used to rebuild three-dimensional space coordinates of matching points so as to achieve wind turbine dynamic response. A laboratory-scale experimental platform is constructed to test the dynamic response of the wind turbine system. In order to verify the accuracy of the proposed method, a three-dimensional model of a wind turbine is built. With dynamic structure response process adopted to carry out dynamic analysis and compare theoretical results with test results, the results vary by less than 10 %, indicating that the test method presented in the paper is feasible and effective.
Re-understanding of data storytelling tools from a narrative perspective

Abstract With a large number of data stories expressed in diverse genres of narrative visualization, storytelling is currently growing in popularity in the field of visualization. In this paper, we investigate the data storytelling literature over the last 10 years and suggest a brand-new classification scheme for authoring tools from narrative perspectives. Our classification scheme comprehensively and meticulously summarizes the collected papers. By arranging papers in each category by publication date, we identify the popular topics, and present the current status and future directions of the development of data storytelling authoring tools. There are two contributions in our paper. First, we propose a novel taxonomy that includes two main categories and four sub-categories to classify narrative perspectives in data storytelling authoring tools. Second, we discuss and highlight research challenges and promising potential future research opportunities. We hope that this survey will promote the discussion of data storytelling tools and provide a reference for researchers who wish to delve more deeply into the research areas covered in this field.
Solution of Real-Time Traffic Signs Detection Problem for Autonomous Vehicles by Using YOLOV4 And Haarcascade Algorithms

Unmanned systems are increasingly used today to facilitate our daily lives and use time more efficiently. Therefore, this rapidly emerging and growing technology appears in every aspect of our lives with its various functions. Object recognition algorithms are one of the most important functions that we often encounter in these systems. Autonomous vehicle technologies are the latest and fastest growing technology among unmanned systems. In this study, we investigate the success rates of two different algorithms for recognizing traffic signs and markings that can be used for partially or fully autonomous vehicles. In this study, two different solutions to the problem of recognizing the signs for fully autonomous and fully autonomous vehicles, respectively, were presented and the correct identification of the markers was evaluated. The work was performed in real-time. Two different concepts were used for these products. An enclosed space where an ideal lighting environment is provided for the evaluation of models should be visualized. In addition, for the general recognition of the models, the test procedures were performed with a dataset obtained from the users and it was computed for the general recognition. In addition, this study aims to provide a better understanding of the basic working principles, the differences between machine learning and deep learning, and the contents of object recognition processes.
Discriminable feature enhancement for unsupervised domain adaptation

Unsupervised domain adaptation addresses the problem of knowledge transformation from source domain to target domain, aiming to effectively alleviate data distribution mismatch and data labeling consumption. The issue of data distribution mismatches is widespread in actual agricultural visual tasks. Moreover, it is expensive and time-consuming to construct and label visual image data. For in-field cotton boll, its maturing status can greatly affect the yield and quality. Uneven distribution restrains the performance for maturing status recognition. Therefore, domain adaptation is essential for identifying cross-domain cotton boll maturity. Existing unsupervised domain adaptation methods obtain domain invariant feature for achieving domain alignment. However, the discriminability of features is less considered, which may result in unsatisfactory classification results. In this paper, an unsupervised domain adaptation method called discriminable feature enhancement(DFE-DA) is proposed to identify cross-domain cotton boll maturity. It enables to minimize intra-class distance by maximizing intra-domain density(MID) loss and realizes discriminable feature enhancement. The effectiveness of DFE-DA is verified on in-field cotton boll V2(IFCB-V2) dataset containing 2400 images. The experimental results demonstrate that DFE-DA has an average improvement of 12.8%, 10.3% and 7.6% compared with other methods in three different transfer tasks. Furthermore, the MID loss can cooperate well with other adversarial methods. To verity the robustness of DFE-DA, additional experiments conducted on the public benchmark Office-31 and Office-Home indicates it is comparable to the state-of-the-arts.
VR training program for fire escape: Learning progress predicted by the perception of fire presence, VR operational frustration, and gameplay self-efficacy

Most VR fire escape training programs only task learners to observe the procedure of fire escape in different simulated fire scenes. To improve the effectiveness of such training programs for everyone, we tested a “fire escape virtual reality training program” which takes advantage of the feedback on the action to help individuals to learn the necessary and correct steps of fire escape. The virtual program emulates a real fire scene by providing realistic visual and auditory stimuli. A single-group quasi-experimental study was carried out to measure the effectiveness of the program, and a total of 173 seventh- and eighth-grade students from a high school in New Taipei City participated. The results of structural equation modeling showed that 1) gameplay self-efficacy was negatively predicted by frustration, 2) fire presence positively predicted gameplay self-efficacy, and 3) gameplay self-efficacy positively predicted learning progress. The findings suggested that critical life-saving skills such as fire escape skills can be readily acquired and trained through individual virtual reality training programs.
Restoring original signals from pile-up using deep learning

Pile-up signals are frequently produced in experimental physics. They create inaccurate physics data with high uncertainties and cause multiple problems. Therefore, the correction of pile-up signals is crucially required. In this study, we implemented a deep learning method to restore the original signals from signals piled up with unwanted signals. We showed that a deep learning model could accurately reconstruct the original signal waveforms from the pile-up waveforms. By substituting the pile-up signals with the original signals predicted by the model, the energy and timing resolution of the data are notably enhanced. The model implementation significantly improved the quality of the particle identification plot and particle tracks. This method is applicable to similar problems, such as separating multiple signals or correcting pile-up signals with other types of noises and backgrounds.
Automatic parking trajectory planning based on random sampling and nonlinear optimization

The current parking trajectory planning algorithms based on geometric connections or formulation of optimization problems in automatic parking systems have strict requirements on the starting position, lower planning efficiency and discontinuous curvature of the reference trajectory. In order to solve these problems, a hierarchical planning algorithm which is combined with nonlinear optimization and the improved RRT* algorithm (Rapidly-exploring Random Tree Star) with Reeds-Shepp curve is proposed in this paper. First, the improved RRT*RS algorithm with the rapid repulsion-straddle experiment is designed for enhancing the efficiency of path planning. Second, because of the shortcomings of the Reeds-Shepp curve that can meet the minimum turning radius but not realize the continuous curvature of the path, a nonlinear optimization problem based on convex-set obstacle constraints is formulated and solved. Finally, simulation results show that the proposed parking trajectory planning algorithm in this paper can plan an effective parking trajectory with continuous curvature in different starting positions and multiple parking scenarios.
Settlement formation along the Angara river (mid 17th and early 21st centuries)

Purpose: Identification of possible settlement formation along the Angara River in the Irkutsk agglomeration at different formation stages and its urban development. Design/methodology/approach : A number of outlined important tasks include the description of each formation stage of settlement with respect to natural resources, urban planning, transport and socio-economic potential; identification of problems and key points of the spatial development of the existing settlement system; solutions of the identified problems. Urban-ecological principles developed by A.G. Bolshakov are used together with the literature in the field. Research findings : The considered existing planning system includes transport, economic and landscape-ecological facilities. Practical implications : The paper suggests possible ways to solve the identified problems. Originality/value : The mentioned facilities can serve as a basis for the settlement formation and spatial development.
Blockchain Technology Participates in the Path and Mode Optimization of Supply Chain Finance

Supply chain finance drives the stable development of supply chain, but there are many risk points and information asymmetry problems in the traditional supply chain finance. In recent years, with the continuous development of blockchain technology, blockchain technology has been introduced in the field of supply chain finance, and its own advantages to solve some problems restricting its development. This paper analyzes the advantages and problems of blockchain technology applied in the process of supply chain finance, and puts forward a new feasible path to optimize the process of blockchain participation in supply chain finance.