Reviewerly/exHarmony huggingface dataset:
Director's intention as the main component of the artistic creative process of developing a holistic stage work

Purpose of Article. The reason of creating a director’s intention as the conceptual component of the artistic and creative process developing a stage form that requires the synthesis of special exercises and accents and a complex of methods in ideological and emotional expressiveness. The methodology consists in the application of artistic, functional, analytical, systematic methods of research interpretative concept of the formation of intention as a concrete understanding and awareness of reality, which defines a clear citizenship position, emotional feeling, as well as the plastic-spatial and tonal solution of the performance in a single stylistic and genre key-way. The scientific novelty of the study is on a sequential analysis of the director’s intention to create an intent stage work, as well as the phased structuring of his paradigm as a component of professional activity at this period of time. Conclusions. As a result of the study, we can conclude that first of all, each director sought and today continues to seek to create his own theater in accordance with their horizons and perceptions in order to form personal creative workshop, find unique, original and actual solutions for the stage production in the successful directorial intention. Secondly, the main goal of the artistic and creative process in developing a director’s intention is the reality of individual metaphor which must always remain. It could be creative, new, friendly, any kind, but grandiose and comprehensive. Last, but not least, despite all its versatility, originality, and certainty as an independent art directing in its base there is an interpretation of a literary work of director's vision which gives its' visual life in the stage through the spatio-temporal dimension.
Distributed randomized multiagent policy iteration in reinforcement learning

We propose a distributed randomized policy iteration algorithm for infinite horizon dynamic programming problems for which the control at each stage is m-dimensional. The traditional policy iteration algorithm involves performing a minimization over an m-dimensional constraint set and has a computational complexity that increases exponentially in m, resulting in an intractable combinatorial search problem. In each iteration, our algorithm performs a series of sequential minimizations followed by policy evaluation and policy improvement using the policy that attains the minimum cost over the sequential minimizations. Our algorithm is well-suited for parallel computation, has a complexity that increases linearly in m, and converges to an agent-by-agent optimal policy. We characterize sufficient conditions for which our algorithm generates a globally optimal policy that coincides with that obtained from standard policy iteration.
Semantic Recommendation Model via Fusing Knowledge Graph and Formal Concept Analysis

The core idea of semantic recommendation is to incorporate semantic knowledge into the recommendation process. The semantic recommendation algorithm, based on knowledge graph, ignores the deep implicit semantics of the evaluation data. The semantic recommendation algorithm based on the deep matrix decomposition model is limited to the implicit semantics of the evaluation data. The semantic recommendation algorithm based on the collaborative filtering algorithm performs only the selection of the nearest neighbors of the user or the item unilaterally and ignores the influence of other aspects, which naturally leads to a decrease in the recommendation accuracy. To solve the above problems, this paper introduces Formal Concept Analysis (FCA) based on collaborative filtering. Using the property that the formal concept in FCA can cluster objects (users) and attributes (items) simultaneously, we propose a semantic recommendation algorithm (SRKGFCA) based on the knowledge graph and formal concept analysis to solve the problem of ignoring user or item factors. Finally, the proposed semantic recommendation algorithm is validated on two public datasets in this work. By using traditional algorithms and current semantic recommendation algorithms as benchmarks, extensive experiments show that our proposed algorithm consistently outperforms state-of-the-art methods.
Wrapper-Based Feature Selection for Medical Diagnosis: The BTLBO-KNN Algorithm

Medical diagnosis research has recently focused on feature selection techniques due to the availability of multiple variables in medical datasets. Wrapper-based feature selection approaches have shown promise in providing faster and more cost-effective predictors. However, selecting the most relevant features from medical datasets to increase disease classification accuracy remains a challenging research issue. To address this challenge, we propose an effective wrapper-based feature selection approach called BTLBO-KNN. It combines an improved Binary Teaching-Learning Based Optimization (BTLBO) algorithm with the K-Nearest Neighbor (KNN) classifier to accelerate the convergence rate in finding the near-optimal features subset. BTLBO-KNN incorporates two new efficient binary teaching and learning processes, an abandoned learner’s replacement mechanism, and a teacher knowledge improvement method. We extensively compare BTLBO-KNN with recent state-of-the-art wrapper-based feature selection approaches on COVID-19 and 23 gene-expression and medical datasets with different dimensional complexities. Our results demonstrate the superiority of BTLBO-KNN over its alternatives in terms of minimizing the number of selected features and the classification error rate.
AI Based Virtual Quiz System

We have suggested an AI-based virtual quiz system that recognises hand gestures and extracts features using a web camera. For students, we have provided Register/Login in this. To take the test, each of them must sign up.To answer the quiz questions in this case, we employed the hand tracking module. This method uses a webcam that is attached to the system to record live footage. Hand segmentation and live video processing are done first. Following segmentation, hand landmarks are tracked to identify hand gestures. The minimal distance between two defined landmarks is determined by comparing the defined gesture in the system with the hand gesture in the live video. As a result, the response will be saved and compared to the model response. The Score is presented at the end of the quiz in percentage form, along with the correct, incorrect, and skipped questions. For those with disabilities, this system is efficient and helpful. Using this cutting-edge gesture recognition technique, the system will shorten the time required for manual exam systems
BIF-hosted high-grade magnetite iron ore targeting by hyperspectral wavelength mapping of chlorite: case study of Qidashan Iron Mine, northeast China

There are two kinds of chlorites occurred in Anshan area, among which the hydrothermal Fe-chlorite with longer Fe-OH wavelength is spatially related to the high-grade magnetite ore, meaning that wavelength mapping of chlorites can be used to target the BIF-hosted high grade iron ore. In this article, quadratic polynomial, cubic spline, and quartic polynomial method were used to interpolate the absorption wavelength near 2250 nm of China ZY1-02D satellite hyperspectral image. The result of quadratic polynomial is continuous without data overlapping or intervals, thus most suitable for discrimination of the chlorites. The field truth shows that the spatial distribution of Fe-chlorite, validated by XRD analysis, is not only in accordance with that of high grade magnetite ore bodies, but also consistent with the recently discovered concealed iron bonanza. The study shows that hyperspectral remote sensing techniques can play significant role in the exploration of magnetite iron ore in east Liaoning Province.
EFFECTIVE APPROACH FOR FACE RECOGNITION AND ACTIVE SHAPE 3D MODELS USING KERNEL PRINCIPAL COMPONENT ANALYSIS

Face Recognition is a computer application that is capable of detecting, tracking, identifying or verifying human faces from an image or video captured using a digital camera. Although lot of progress has been made in domain of face detection and recognition for security, identification and surveillance purpose, but still there are issues hindering the progress to reach or surpass human level accuracy. These issues are variations in human facial appearance such as; varying lighting condition, noise in face images, scale, pose etc. Kernel principal component analysis (KPCA) as a powerful nonlinear feature extraction method has proven as a preprocessing step for classification algorithm. A face recognition approach based on KPCA and genetic algorithms (GAs) is proposed. By the use of the polynomial functions as a kernel function in KPCA, the high order relationships can be utilized and the nonlinear principal components can be obtained. After that nonlinear principal components, we use GAs to select the optimal feature set for classification.
Application of mineral chemistry in determination of protolith and thermobarometry of the Sartip Abad metamorphic rocks (Qorveh-Kurdistan Province)

Application of mineral chemistry in determination of protolith and thermobarometry of the Sartip Abad metamorphic rocks (Qorveh-Kurdistan Province)
Rigorous and integrated self-calibration model for a large-field-of-view camera using a star image

This paper proposes a novel self-calibration method for a large-FoV (Field-of-View) camera using a real star image. First, based on the classic equisolid-angle projection model and polynomial distortion model, the inclination of the optical axis is thoroughly considered with respect to the image plane, and a rigorous imaging model including 8 unknown intrinsic parameters is built. Second, the basic calibration equation based on star vector observations is presented. Third, the partial derivative expressions of all 11 camera parameters for linearizing the calibration equation are deduced in detail, and an iterative solution using the least squares method is given. Furtherly, simulation experiment is designed, results of which shows the new model has a better performance than the old model. At last, three experiments were conducted at night in central China and 671 valid star images were collected. The results indicate that the new method obtains a mean magnitude of reprojection error of 0.251 pixels at a 120° FoV, which improves the calibration accuracy by 38.6% compared with the old calibration model (not considering the inclination of the optical axis). When the FoV drops below 20°, the mean magnitude of the reprojection error decreases to 0.15 pixels for both the new model and the old model. Since stars instead of manual control points are used, the new method can realize self-calibration, which might be significant for the long-duration navigation of vehicles in some unfamiliar or extreme environments, such as those of Mars or Earth's moon.
Object-Level Data-Driven Sensor Simulation for Automotive Environment Perception

The gradually evolving automated driving and ADAS functions require more enhanced environment perception. The key to reliable environmental perception is large amounts of data that are hard to collect. Several simulators provide realistic, raw sensor data based on physical sensor models. However, besides their high price, they also require very high computation capacity. Furthermore, most sensor suppliers provide high-level data, such as object detections, that is complicated to reproduce from simulated raw sensor data. This paper proposes a method that directly simulates the detections or object tracks provided by smart sensors. The model involves several uncertainties of the sensors, such as missed-, false detections, and measurement noise. In contrast to the conventional sensor models, this method tackles with state-dependent clutter model and considers the field of view in the detections model. The parameters of the proposed model are identified for an automotive smart radar and camera based on pre-evaluated real-world measurements. The resulting model provides synthetic object-level data with higher fidelity than the conventional probabilistic models, differing less than 2% from the precision and recall metrics of the actual sensors.
An efficient single shot detector with weight-based feature fusion for small object detection

Object detection has been widely applied in various fields with the rapid development of deep learning in recent years. However, detecting small objects is still a challenging task because of the limited information in features and the complex background. To further enhance the detection accuracy of small objects, this paper proposes an efficient single-shot detector with weight-based feature fusion (WFFA-SSD). First, a weight-based feature fusion block is designed to adaptively fuse information from several multi-scale feature maps. The feature fusion block can exploit contextual information for feature maps with large resolutions. Then, a context attention block is applied to reinforce the local region in the feature maps. Moreover, a pyramids aggregation block is applied to combine the two feature pyramids to classify and locate target objects. The experimental results demonstrate that the proposed WFFA-SSD achieves higher mean Average Precision (mAP) under the premise of ensuring real-time performance. WFFA-SSD increases the mAP of the car by 4.12% on the test set of the CARPK.
Use of Design Sprint in PBL Modules– A Longitudinal Case Study Approach

Project Based Learning (PBL) modules have become an inevitable part in SE curriculums. Industry relevance of PBL mechanisms have further strengthened this crucial need. Design Sprint is well known as a PBL approach which enhances transferable skills in students. Design Sprint is also a widely used mechanism in software industry projects. Therefore, Design Sprint could be used to enhance the outcomes of SE undergraduate PBL projects. This study makes use of a longitudinal case study approach to investigate the effectiveness of using Design Sprint approach in a SE undergraduate module which used PBL. The study focusses a group of students (111) and academics (12) in SE degree program in a private higher education institute in Sri Lanka. Qualitative data gathered from the target audience in two distinctive occasions to investigate the effectiveness of Design Sprint in PBL projects. The results showcased that Design Sprint has a positive impact in improving performance criteria such as problem solving, teamwork, analytical skills, communication skills and acquiring new knowledge. The results indicated that majority of the students (more than 80%) had positive opinions about the Design Sprint. The academics further strengthened this finding as they also agree that there was a significant improvement in student performance in PBL projects after introducing Design Sprint mechanism.
Enhancing Feature Selection for Imbalanced Alzheimer’s Disease Brain MRI Images by Random Forest

Imbalanced learning problems often occur in application scenarios and are additionally an important research direction in the field of machine learning. Traditional classifiers are substantially less effective for datasets with an imbalanced distribution, especially for high-dimensional longitudinal data structures. In the medical field, the imbalance of data problem is more common, and correctly identifying samples of the minority class can obtain important information. Moreover, class imbalance in imbalanced AD (Alzheimer’s disease) data presents a significant challenge for machine learning algorithms that assume the data are evenly distributed within the classes. In this paper, we propose a random forest-based feature selection algorithm for imbalanced neuroimaging data classification. The algorithm employs random forest to evaluate the value of each feature and combines the correlation matrix to choose the optimal feature subset, which is applied to imbalanced MRI (magnetic resonance imaging) AD data to identify AD, MCI (mild cognitive impairment), and NC (normal individuals). In addition, we extract multiple features from AD images that can represent 2D and 3D brain information. The effectiveness of the proposed method is verified by the experimental evaluation using the public ADNI (Alzheimer’s neuroimaging initiative) dataset, and results demonstrate that the proposed method has a higher prediction accuracy and AUC (area under the receiver operating characteristic curve) value in NC-AD, MCI-AD, and NC-MCI group data, with the highest accuracy and AUC value for the NC-AD group data.
Metasubject approach to teaching geography on the example of studying the life of the Eskimos

The purpose of the work is to substantiate the meta-subject approach to teaching geography on the example of studying the life of ethnic groups. Based on the methods of analysis of psychological and pedagogical literature and technological maps of lessons taken from teachers' websites; designing the meta-subject content of educational tasks in the course of school geography, the need to use the subject and meta-subject approach in their totality was identified and justified. The author's assignments for the development of basic logical actions among students and the formation of interdisciplinary concepts were developed with the involvement of knowledge, methods of activity and concepts of related disciplines on the example of studying the life and life of the Eskimos in the subarctic tundra. The meta-subject approach made it possible to realize the pedagogical potential of geography in the educational process using the knowledge of history, biology, physics, and chemistry. Interdisciplinary concepts were revealed in the study of the dwellings of the Eskimos. The results of the study were tested in the system of advanced training for teachers of geography, biology, chemistry, physics; in the system of training future teachers of geography and ecology. The developed complex integrated tasks, in which universal learning activities require a logical continuation using interdisciplinary concepts, can be used in the study of disciplines at the bachelor’s-lauric level “Project activity in education”, the master’s program “Formation of meta-subject learning outcomes in geography”.
Analysis of the Whiplash gradient descent dynamics

Abstract In this paper, we propose the Whiplash inertial gradient dynamics, a closed‐loop optimization method that utilizes gradient information. We introduce the symplectic asymptotic convergence analysis for the Whiplash system for convex functions. We also introduce relaxation sequences to explain the nonclassical nature of the algorithm and an exploring heuristic variant of the Whiplash algorithm to escape saddle points, deterministically. We provide a practical methodology for analyzing convergence rates using integral constraint bounds and a novel Lyapunov rate method. Our results demonstrate polynomial and exponential rates of convergence for quadratic costs.
Integrated Development of Embedded Systems With Remote Graphical User Interfaces

This paper presents a Graphical User Interface (GUI) Builder for embedded systems and Cyber-Physical Systems (CPSs). The hardware platforms used in CPSs may employ physical devices without graphical and user-input capabilities, or devices that may be placed on multiple remote locations, often with difficult physical access, that brings new challenges to GUI design. The proposed tool, integrated within the IOPT-Flow framework, enables a new approach to answer these challenges. This framework supports the development of embedded systems and distributed CPSs, offering a set of web-based tools for model edition, simulation, and implementation on physical devices. Systems are designed using graphical models combining Data-flows, Signals, and Petri nets, that permit the specification of remote communication channels just by drawing arcs. The new GUI Builder tool uses this infrastructure to automate the creation of graphical user interfaces for embedded systems and distributed CPSs, benefiting from a development with combined design, validation and automatic code generation methods. Finally, an application example of a power wheelchair controller integrating a GUI, is presented.
Virtual reality eye movements are not inferior to computerized eye movements and exposure in ameliorating aversive memories

Lab studies show that the emotionality and vividness of aversive memories decrease as a result of engaging in a working memory task during the recall of that memory. Translation of the dual tasking working mechanism to a VR program would allow for future VR-EMDR clinical and dissemination trials. The current study took the first step towards that goal by assessing the non-inferiority of a Virtual Reality (VR) eye movement task compared to computerized eye movements in amelioration of aversive autobiographical memory in a student sample. In a cross-over design, university students (N = 195) recalled three aversive autobiographical memories and received VR Eye Movements (VR EM), Computerized Eye Movements (EM), and Exposure Only (EO). Emotionality and vividness of the memories were assessed before and after each condition. We found VR EM to be non-inferior to Computerized EM and EO in reducing emotionality and vividness. Both EM conditions outperformed the EO condition. Against our expectations, we found steeper declines of emotionality scores in the VR EM condition compared to the Computerized EM. Lastly, we found a sensitization effect in our EO condition: emotionality and vividness increased due to this procedure. Despite our efforts in standardizing working memory taxation between the dual tasking conditions (such as viewing angle and speed of eye movements), characteristics of the VR eye movements task might have unintentionally loaded additional working memory. These findings show that VR eye movements could be a viable dual task and might serve as the first step to research VR eye movements in clinical practice.
WDIG: a wavelet domain image generation framework based on frequency domain optimization

Abstract In the end-to-end image generation task, the spatial domain of pixel space cannot explicitly separate the low-frequency general information such as texture and color from the high-frequency detail information such as structure and identity. The loss function calculated in the spatial domain fails to effectively constrain the maintenance of detail information, and the generated image quality is insufficient. In this paper, a wavelet domain image generation (WDIG) framework is proposed to preserve the frequency information of images, in which the loss functions are constructed in the pixel space and wavelet space. In the pixel space, the low-frequency and high-frequency characteristic information of the signal are obtained by setting the appropriate Gaussian kernel and adopting the Gaussian fuzzy method. The loss function of $$\ell_{1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> norm spatial domain is constructed for the low-frequency and high-frequency characteristic information. In the wavelet space, the corresponding channel sub-band coefficients are obtained by wavelet transform, and the image is explicitly separated into high-frequency information and low-frequency information. The $$\ell_{1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> norm frequency domain loss function is constructed respectively for the sub-band coefficients. The WDIG can constrain model training more accurately and optimize model more precisely, so as to better maintain the details and quality of generated image. The WDIG framework is evaluated in the image generation applications including style transfer, image translation and Generative Adversarial Nets (GAN) Inversion. Experimental results show that the WDIG framework can effectively retain the details of images and generate more realistic images, and improve the image quality of the above applications in image generation.