Reviewerly/exHarmony huggingface dataset:
Reflection On Development Of Scientific-Educational Knowledge And Terminology

The authors attempt identifying features in formation and development of scientific and educational knowledge as a gnoseologic category from the point of view of the ontological approach in both synchrony and diachrony, thus opening a possibility to determine ontological attributes, properties, structure of cognition, foundation of thought development in a subject.The authors distinguish between scientific cognition and educational cognition.Applying methodological optics, reflecting heterogeneous context and varying socio-cultural environment of the subject of a modern education, the authors provide philosophical justifications for appearance of scientific and educational cognition, bringing to lite conceptual and connotation-related features of concepts, systematize the sphere of application of the knowledge through the lens of the subject's mental state on the basis of specific and modified general knowledge, both generalized and obtained from personal experience.The paper describes principal factors that determine reinterpretation of the education and personal development as phenomena in the scientific thesaurus of Russian and foreign educational science; the role of personal existential experience in the context of social personality development is emphasized.Making accents on hermeneutic content of educational terms reflecting the permanent process of subjective spiritual activity allows understanding these terms as a universal environment for designation of a holographic spectrum of phenomena in pedagogical science.Development of terminology is seen as an interaction during which new thought forms arise and then adapted to a linguistic system.
Jungian Psychological Types Theory: Introduction To The New Cognitive Conception

The paper discusses the theory of psychological types (Jungian Psychological Types Theory), developed by the Swiss psychologist and psychiatrist Carl Gustav Jung.Original interpretations of Jung's theory were given in the works of American researchers Katharine Cook Briggs and Isabel Briggs Myers, as well as the Soviet researcher from Lithuania Aushra Augustinavichiute, in which Jung's initial ideas gained their conceptual development.The concept of Briggs and Briggs Myers, as well as the concept of Aushra Augustinavichiute, are two directions that can be conditionally called the "western" branch of the Jungian.The "eastern" branch is represented by the concept of Aushra Augustinavichiute, within which a cybernetic model of the psychological type (model A) has been created, the theory of intertype relations has been developed, and the terminological apparatus of Jung's theory has been partially updated. Scientists of Moscow InternationalUniversity on the basis of the Department of Psychology and the "Laboratory of Cognitive Research of Consciousness" have developed new typological conception, within which a new terminological apparatus and a new way of representing and describing a psychological type have been elaborated.Our concept allows accelerating the further development of Jungian Psychological Types Theory in the 21 st century.
Continuity For Research Skill Development In Bachelor And Master Programs

The system of higher professional education, designed to provide society with highly qualified personnel, seeks to comply with the principle of bachelor-master continuity for successful functioning.This crosscutting task is geared toward both the continuity of academic content and consistent development of functional competences at each level of education.In this regard, research skills are viewed to indicate that future graduates are able and willing to perform scientific and professional activities.Based on system and activity approaches, as well as the theory of constructivism, the paper highlights search, analytical and presentation stages by which this type of skills develops, on the example of working with foreign-language scientific sources.The levels of research skills (low, medium and high) were developed separately for bachelor and master programs.These levels clearly show progression towards diversity, depth and range of complexity.Depicting successive development of research skills as a constructive sequential solution of research problems, the following principles can be delineated, namely: raising students' awareness of the activity performed; increasing students' autonomy as they master research skills; transfer of research expertise from university to employment.The need to follow the principle of recursivity is particularly emphasized when a student takes the stages of development of research skills.The above stages and levels of research skills, as well as ideas behind their development, give an opportunity to present continuous development of research skills in bachelor and master programs.
Complexity of planning for connected agents in a partially known environment

The Connected Multi-Agent Path Finding (CMAPF) problem asks for a plan to move a group of agents in a graph while respecting a connectivity constraint. We study a generalization of CMAPF in which the graph is not entirely known in advance, but is discovered by the agents during their mission. We present a framework introducing this notion and study the problem of searching for a strategy to reach a configuration in this setting. We prove the problem to be PSPACE-complete when requiring all agents to be connected at all times, and NEXPTIME-hard in the decentralized case.
Two-stream temporal enhanced Fisher vector encoding for skeleton-based action recognition

Abstract The key to skeleton-based action recognition is how to extract discriminative features from skeleton data. Recently, graph convolutional networks (GCNs) are proven to be highly successful for skeleton-based action recognition. However, existing GCN-based methods focus on extracting robust features while neglecting the information of feature distributions. In this work, we aim to introduce Fisher vector (FV) encoding into GCN to effectively utilize the information of feature distributions. However, since the Gaussian Mixture Model (GMM) is employed to fit the global distribution of features, Fisher vector encoding inevitably leads to losing temporal information of actions, which is demonstrated by our analysis. To tackle this problem, we propose a temporal enhanced Fisher vector encoding algorithm (TEFV) to provide more discriminative visual representation. Compared with FV, our TEFV model can not only preserve the temporal information of the entire action but also capture fine-grained spatial configurations and temporal dynamics. Moreover, we propose a two-stream framework (2sTEFV-GCN) by combining the TEFV model with the GCN model to further improve the performance. On two large-scale datasets for skeleton-based action recognition, NTU-RGB+D 60 and NTU-RGB+D 120, our model achieves state-of-the-art performance.
A method for high dynamic range 3D color modeling of objects through a color camera

Abstract This paper proposes a novel method for enhancing the dynamic range of structured-light cameras to solve the problem of highlight that occurs when 3D modeling highly reflective objects using the structured-light method. Our method uses the differences in quantum efficiency between R, G, and B pixels in the color image sensor of a monochromatic laser to obtain structured-light images of an object under test with different luminance values. Our approach sacrifices the resolution of the image sensor to increase the dynamic range of the vision system. Additionally, to enhance our system, we leverage the backgrounds of structured-light stripe pattern images to restore the color information of measured objects, whereas the background is often removed as noise in other 3D reconstruction systems. This reduces the number of cameras required for 3D reconstruction and the matching error between point clouds and color data. We modeled both highly reflective and non-highly reflective objects and achieved satisfactory results.
Object detection on robosoccer environment using convolution neural network

Robots with autonomous capabilities depend on vision capabilities to detect and interact with objects and their environment. In the field of robotic research, one of the focus areas is the robosoccer platform that is being used to implement and test new ideas and findings on computer vision and decision making. In this article, an efficient real-time object detection algorithm is employed in a robosoccer simulation environment by deploying a convolution neural network and Kalman filter based tracking algorithms. This study's objective is to classify nao, ball, and the goalpost as well as to validate nao and ball tracking without human intervention from initial frame to last frame. In comparison with the existing methods, the proposed method is robust and fast in identifying three classes namely nao, ball, and goalpost with a speed of 1.67 FPS and a mAP of 95.18%. By implementing this approach, soccer playing robots can make appropriate decisions during game play.
STUDENTS’ ABILITY TO CONDUCT PRE-EDITING OF TEXT PROCEDURE FOR GOOGLE NEURAL MACHINE TRANSLATION

This study aimed to find out students’ ability to conduct pre-editing on the text procedure inputs to Google Neural Machine Translation (GNMT). The participants in this study were students of the English Education Program, Faculty of Teacher Training and Education, Mataram University that took “Translation and Interpreting” subject in the fourth semester in Academic Year 2021/2022. The data were collected from assignments completed by 26 students in class A. Supporting data were collected through observation during the class. The data were analyzed using content analysis procedures such as identifying, categorizing, describing, and explaining. The results of this study indicate that almost all students could conduct pre-editing, but not perfectly, and some students failed to conduct pre-editing on the text. The result of pre-editing looked like a revised version of text. The pre-editing shows how the source text changes especially in language structure, word choice, and punctuation. The ability of the students in conducting the pre-editing on the the source text is represented by the good or bad quality of translated text by GNMT. Thus, the more effort performed in the pre-editing of the text context, the more likely it is to generate text to be in better quality in translation by GNMT.
Initialization of Feature Selection Search for Classification

Selecting the best features in a dataset improves accuracy and efficiency of classifiers in a learning process. Datasets generally have more features than necessary, some of them being irrelevant or redundant to others. For this reason, numerous feature selection methods have been developed, in which different evaluation functions and measures are applied. This paper proposes the systematic application of individual feature evaluation methods to initialize search-based feature subset selection methods. An exhaustive review of the starting methods used by genetic algorithms from 2014 to 2020 has been carried out. Subsequently, an in-depth empirical study has been carried out evaluating the proposal for different search-based feature selection methods (Sequential forward and backward selection, Las Vegas filter and wrapper, Simulated Annealing and Genetic Algorithms). Since the computation time is reduced and the classification accuracy with the selected features is improved, the initialization of feature selection proposed in this work is proved to be worth considering while designing any feature selection algorithms.
Iraqi Paradigm E-Voting System Based on Hyperledger Fabric Blockchain Platform

Voting is one of the most fundamental components of a democratic society. In 2021 Iraq held the Council of Representatives (CoR) elections in 83 electoral constituencies in 19 governorates. Nonetheless, several significant issues arose during this election, including the problem of logistics distribution, the excessively long period of ballot counting, voters can't know if their votes were counted or if their ballots were tampered with, and the inconsistent regulation of vote counting. Blockchain technology, which was just invented, may offer a solution to these problems. This paper introduces an electronic voting system for the Iraq Council of Representatives elections that is based on a prototype of the permission hyperledger fabric blockchain. An immutable, distributed ledger maintained by all members of a network is what blockchain technology is all about. By authenticating each voter, the system can prevent voting fraud by making votes traceable and verifiable, hence decreasing the chance of unlawful activities and fraudulent ballots. This work investigates the influence of E-voting, specifically the voting phase workload, on the performance of the hyperledger fabric blockchain platform in terms of latency and throughput by altering transaction send rates (tps), block size, and block timeout.
Corrigendum: Role of leadership in adoption of blockchain technology in small and medium enterprises in Saudi Arabia

[This corrects the article DOI: 10.3389/fpsyg.2022.911432.].
Robust three-qubit search algorithm in Rydberg atoms via geometric control

Rydberg atoms possess long coherence time and inherent scalability, which makes it promising to implement quantum algorithms. An exact and robust quantum search algorithm (SA) is essential to some practical applications. Here we propose a multisolution three-qubit SA by employing quantum circuit and geometric operations, in which the target states can be successfully searched with the fidelity of at least 99.8$%$ and the geometric operators guarantee robustness against systematic errors. In particular, the geometric three-qubit gate operators employed reduce the implementation time and help to resist against the detrimental influence of decoherence. Moreover, our scheme can be straightforwardly extended to multiqubit cases. We have carried out numerical simulations based on the master equation to illustrate the superiority of our scheme. We consider that our study provides an alternative method for executing quantum SAs with high success probability.
The Mental Lexicon Features of the Hakka-Mandarin Dialect Bilingual

The current study investigated the mental lexicon features of the Hakka-Mandarin dialect bilingual from two perspectives: the structural features of lexicons and the relations between lexicons. Experiment one used a semantic fluency task and complex-network analysis to observe the structural features of lexicons. Experiment two used a cross-language long-term repetition priming paradigm to explore the relations between lexicons, with three sub-experiments focusing on conceptual representation, lexical representation, and their relations, respectively. The results from experiment one showed that the dialect bilingual lexicons were small-world in nature, and the D2 (Mandarin) lexicon was better organized than the D1 (Hakka) lexicon. Experiment two found that D1 and D2 might have partially shared conceptual representations, separate lexical form representations, and partially shared lemma representations. Based on the findings, we tentatively proposed a two-layer activation model to simulate the lexicon features of dialect bilingual speakers.
Distilled and Contextualized Neural Models Benchmarked for Vulnerable Function Detection

Detecting vulnerabilities in programs is an important yet challenging problem in cybersecurity. The recent advancement in techniques of natural language understanding enables the data-driven research on automated code analysis to embrace Pre-trained Contextualized Models (PCMs). These models are pre-trained on the large corpus and can be fine-tuned for various downstream tasks, but their feasibility and effectiveness for software vulnerability detection have not been systematically studied. In this paper, we explore six prevalent PCMs and compare them with three mainstream Non-Contextualized Models (NCMs) in terms of generating effective function-level representations for vulnerability detection. We found that, although the detection performance of PCMs outperformed that of the NCMs, training and fine-tuning PCMs were computationally expensive. The budgets for deployment and inference are also considerable in practice, which may prevent the wide adoption of PCMs in the field of interest. However, we discover that, when the PCMs were compressed using the technique of knowledge distillation, they achieved similar detection performance but with significantly improved efficiency compared with their uncompressed counterparts when using 40,000 synthetic C functions for fine-tuning and approximately 79,200 real-world C functions for training. Among the distilled PCMs, the distilled CodeBERT achieved the most cost-effective performance. Therefore, we proposed a framework encapsulating the Distilled CodeBERT for an end-to-end Vulnerable function Detection (named DistilVD). To examine the performance of the proposed framework in real-world scenarios, DistilVD was tested on four open-source real-world projects with a small amount of training data. Results showed that DistilVD outperformed the five baseline approaches. Further evaluations on multi-class vulnerability detection also confirmed the effectiveness of DistilVD for detecting various vulnerability types.
A Probabilistic–Geometric Approach for UAV Detection and Avoidance Systems

This paper proposes a collision avoidance algorithm for the detection and avoidance capabilities of Unmanned Aerial Vehicles (UAVs). The proposed algorithm aims to ensure minimum separation between UAVs and geofencing with multiple no-fly zones, considering the sensor uncertainties. The main idea is to compute the collision probability and to initiate collision avoidance manoeuvres determined by the differential geometry concept. The proposed algorithm is validated by both theoretical and numerical analysis. The results indicate that the proposed algorithm ensures minimum separation, efficiency, and scalability compared with other benchmark algorithms.
Semantic Segmentation Algorithm-Based Calculation of Cloud Shadow Trajectory and Cloud Speed

Cloud covering is an important factor affecting solar radiation and causes fluctuations in solar energy production. Therefore, real-time recognition and the prediction of cloud covering and the adjustment of the angle of photovoltaic panels to improve power generation are important research areas in the field of photovoltaic power generation. In this study, several methods, namely, the principle of depth camera measurement distance, semantic segmentation algorithm, and long- and short-term memory (LSTM) network were combined for cloud observation. The semantic segmentation algorithm was applied to identify and extract the cloud contour lines, determine the feature points, and calculate the cloud heights and geographic locations of the cloud shadows. The LSTM algorithm was used to predict the trajectory and speed of the cloud movement, achieve accurate and real-time detection, and track the clouds and the sun. Based on the results of these methods, the shadow area of the cloud on the ground was calculated. The recursive neural LSTM network was also used to predict the track and moving speed of the clouds according to the cloud centroid data of the cloud images at different times. The findings of this study can provide insights to establish a low-cost intelligent monitoring predicting system for cloud covering and power generation.
Learning Deep Representations of Cardiac Structures for 4D Cine MRI Image Segmentation through Semi-Supervised Learning

Learning good data representations for medical imaging tasks ensures the preservation of relevant information and the removal of irrelevant information from the data to improve the interpretability of the learned features. In this paper, we propose a semi-supervised model-namely, combine-all in semi-supervised learning (CqSL)-to demonstrate the power of a simple combination of a disentanglement block, variational autoencoder (VAE), generative adversarial network (GAN), and a conditioning layer-based reconstructor for performing two important tasks in medical imaging: segmentation and reconstruction. Our work is motivated by the recent progress in image segmentation using semi-supervised learning (SSL), which has shown good results with limited labeled data and large amounts of unlabeled data. A disentanglement block decomposes an input image into a domain-invariant spatial factor and a domain-specific non-spatial factor. We assume that medical images acquired using multiple scanners (different domain information) share a common spatial space but differ in non-spatial space (intensities, contrast, etc.). Hence, we utilize our spatial information to generate segmentation masks from unlabeled datasets using a generative adversarial network (GAN). Finally, to reconstruct the original image, our conditioning layer-based reconstruction block recombines spatial information with random non-spatial information sampled from the generative models. Our ablation study demonstrates the benefits of disentanglement in holding domain-invariant (spatial) as well as domain-specific (non-spatial) information with high accuracy. We further apply a structured L2 similarity (SL2SIM) loss along with a mutual information minimizer (MIM) to improve the adversarially trained generative models for better reconstruction. Experimental results achieved on the STACOM 2017 ACDC cine cardiac magnetic resonance (MR) dataset suggest that our proposed (CqSL) model outperforms fully supervised and semi-supervised models, achieving an 83.2% performance accuracy even when using only 1% labeled data. We hypothesize that our proposed model has the potential to become an efficient semantic segmentation tool that may be used for domain adaptation in data-limited medical imaging scenarios, where annotations are expensive. Code, and experimental configurations will be made available publicly.
Lightweight CNN-Based Method for Spacecraft Component Detection

Spacecraft component detection is essential for space missions, such as for rendezvous and on-orbit assembly. Traditional intelligent detection algorithms suffer from drawbacks related to high computational burden, and are not applicable for on-board use. This paper proposes a convolutional neural network (CNN)-based lightweight algorithm for spacecraft component detection. A lightweight approach based on the Ghost module and channel compression is first presented to decrease the amount of processing and data storage required by the detection algorithm. To improve feature extraction, we analyze the characteristics of spacecraft imagery, and multi-head self-attention is used. In addition, a weighted bidirectional feature pyramid network is incorporated into the algorithm to increase precision. Numerical simulations show that the proposed method can drastically reduce the computational overhead while still guaranteeing good detection precision.