Reviewerly/exHarmony huggingface dataset:
Unsuccessful and successful complex problem solvers – A log file analysis of complex problem solving strategies across multiple tasks

Complex problem solving (CPS) is a key competence in educational contexts with strong conceptual links to students' overall intelligence. However, the mechanisms underlying successful CPS are not fully understood. Therefore, this study investigated several factors presumed to be relevant to CPS success using log file data to code each individual student action during six CPS tasks with different characteristics (N = 1276). We coded individual strategy combinations per student for each item for different strategy combinations of vary-one-thing-at-a-time (VOTAT), hold-one-thing-at-a-time (HOTAT), vary-no-thing-at-a-time (NOTAT), and change-all (CA). Results from generalized linear mixed models showed that CPS success was likely to be achieved by using VOTAT. However, there was an increased chance of solving an item when additional strategies, such as NOTAT or NOTAT plus HOTAT were used. This result was moderated by the presence/absence of eigendynamics as an important determinant of item difficulty. Strategy combinations of VOTAT together with other strategies (all including NOTAT) showed higher chances of CPS success when eigendynamics were present. Additionally, higher chances of solving an item when using VOTAT with additional strategies was demonstrated for items without eigendynamics. Overall, our results suggest that flexibility in strategy application is the driving force for successful CPS performance. Implications are discussed in light of the presumed benefit of pedagogically relevant metacognitive skills, such as planning, monitoring and reflecting, for CPS success. Based on our findings, we provide specific recommendations for the development of computer-based learning simulations to train CPS and related competencies, ultimately enhancing students' skills in educational contexts.
Optimal Compressing and Decompressing Digital-Ink Handwriting via Sparse Gaussian Process Regression and Dynamic Programming

In this study, we delve into the challenge of compressing and decompressing digital-ink data obtained from handwritten inputs on devices such as pen tablets, etc. We present a new approach using sparse Gaussian process regression in order to compress digital-ink data into some kernel matrix consisting of a sequence of pseudo-inputs. Moreover, to enhance the accuracy of compression and decompression, we introduce a dynamic programming (DP) approach for the optimal selection of dominant pseudo-inputs. The effectiveness of our method is substantiated through a series of experimental studies.
A Multimodal Document Viewer in Fully Immersive Virtual Reality

Immersive learning using fully immersive virtual reality (VR) is typically utilized to present 3D objects that cannot be represented easily on a computer screen. In the last few decades, the use of e-books to visualize documents has become prevalent. However, transferring an original document designed to be printed requires extraction of the document’s logical structure. The document must be broken down into fragments of text and images with the extraction of its logical structure. The fragments are then recomposed together to be presented as an e-book. In this paper, we propose a novel paradigm where documents can be presented in their original form without the need to be physically restructured, thanks to VR. With a VR headset, the proposed document visualizer allows users to access, display, and annotate documents. Such a system is particularly relevant for students and scholars in the humanities who investigate ancient documents where the distinct elements composing the document are intertwined. Furthermore, VR technology enhances the accessibility and equity of learning by offering various input modalities.
Factors Affecting Junior High School Students' Scientific Literacy: An Empirical Study

This research goals to find out the effect of factors that affecting Junior High School scientific literacy such as reading habit, learning motivation, learning achievement, parental attention and creativity, and the effect between each variable. This empirical study used the PLS-SEM in data analysis with SmartPLS 3.0 version app. The number of study samples was 270 students. The samples were taken by probability sampling technique with stratified random sampling type by school accreditation. Questionnaire and test techniques were employed in data collection. The output revealed that there were significant effect of reading habit and creativity on scientific literacy, with a t-value ≥ t-statistic of 1.96. There was no significant effect of learning motivation, learning achievement and parental attention on scientific literacy, with a t-value ≤ t-statistic of 1.96. In addition, there were significant effect of reading habit on learning motivation and creativity, parental attention to reading habit, learning motivation and learning achievement, and learning motivation to learning achievement and creativity.
A Serious Game Based on Hidden Objects for Art History in Fully Immersive Virtual Reality

Fully immersive virtual reality has been recently used for the creation of virtual museums, art galleries, and other applications related to cultural heritage. While the content of these applications typically has an educational value, it remains difficult to engage users into art and the humanities by only presenting the artworks and their descriptions through an immersive experience. We propose in this paper to better engage users in the analysis of paintings through a serious game based on the principle of wimmelbooks and hidden picture books, e.g., “Where is Waldo”. This serious game provides the user with different art galleries in which the player is asked to search for a piece of painting inside a collection of paintings, engaging the players in the visual analysis of paintings. The pieces of paintings are selected manually, and we also assess the extent to which machine learning may help in defining regions of interest such as faces as potential targets. The evaluation of the approach is a practical example of how serious games can be used for better engaging users into art history in immersive learning.
Safe Exploration in Reinforcement Learning for Learning from Human Experts

This work introduces a new reinforcement learning (RL) method, which has safe exploration and estimates uncertainty in state-action pairs using Monte Carlo (MC) dropout. The proposed method outperforms biased exploration in terms of reward obtained during training. The study also investigates the sensitivity of the algorithm to the uncertainty threshold hyperparameter, suggesting that a lower value leads to a safer policy, but a higher value can result in faster convergence. The proposed algorithm is evaluated in guiding a 2 degree-of-freedom planar robot in its task-space, showing that it can converge to an optimal policy while ensuring safety constraints are met.
Research on image matching method based on improved Salp Swarm Algorithm optimization algorithm

Aiming at the problems of low matching rate and slow speed of traditional image matching methods, an image matching method (MSSA) based on the mutual learning mechanism based on the optimization algorithm of salp swarm optimization was proposed. The traditional salp swarm algorithm has the disadvantages of low optimization accuracy and poor population diversity at the later stage of the iteration, by introducing a mutual learning mechanism, the problem of low population diversity of the salp swarm optimization at the later stage of the iteration is solved, and the convergence speed and accuracy of the algorithm are improved. the improved salp swarm algorithm is transformed into a target matching process. The histogram of oriented gradient is used to extract feature from image blocks to achieve accurate target matching. Finally, 10 classical test functions and CEC2014 test set were used to evaluate the effectiveness of the algorithm. The experimental results show that the improved salp swarm algorithm has a good application in image matching.
Design and Implementation of Physical Education Teaching Management System Based on Multi-agent Model

Abstract The traditional physical education (PE) teaching management system is usually controlled and managed by a single center, which cannot meet the diversified and personalized teaching needs. Therefore, the research of PE teaching management system based on multi-agent mode has become an important direction. The purpose of this paper was to discuss how to improve the effect and quality of PE teaching and enhance students' learning enthusiasm and initiative through the design of multi-agent mode PE teaching management system. The PE teaching management system based on multi-agent mode provides more flexible and personalized teaching management services by utilizing the cooperation and interaction between agents, realizes the information exchange between teachers and students, provides real-time teaching feedback and evaluation, and promotes the sharing and collaboration of teaching resources. Therefore, the operating efficiency of the conventional physical education management system was the highest at 75% and the lowest at 67%, according to the experimental findings of this paper. The multi-agent model-based management system for physical education had a 95 percent maximum operating efficiency and an 88% minimum operational efficiency. The minimum difference between the two was 21%, and the maximum difference was 20%. It can be seen that the operation efficiency of the physical education management system based on the multi-agent model is more advantageous and more stable.
Employing synthetic data for addressing the class imbalance in aspect-based sentiment classification

The class imbalance problem, in which the distribution of different classes in training data is unequal or skewed, is a prevailing problem. This can lead to classifier algorithms being biased, negatively impacting the performance of the minority class. In this paper, we addressed the class imbalance problem in datasets for aspect-based sentiment classification. Aspect-based Sentiment Classification (AbSC) is a type of fine-grained sentiment analysis in which sentiments about particular aspects of an entity are extracted. In this work, we addressed the issue of class imbalance by creating synthetic data. For synthetic data generation, two techniques have been proposed: paraphrasing using the PEGASUS fine-tuned model and backtranslation using the M2M100 neural machine translation model. We compared these techniques with two other class balancing techniques, such as weighted oversampling and cross-entropy loss with class weight. An extensive experimental study has been conducted on three benchmark datasets for restaurant reviews: SemEval-2014, SemEval-2015, and SemEval-2016. We applied these methods to the BERT-based deep learning model for aspect-based sentiment classification and studied the effect of balancing the data on the performance of these models. Our proposed balancing technique, using synthetic data, yielded better results than the other two existing methods for dealing with multi-class imbalance.
FUNDAMENTALS OF LEGISLATIVE REGULATION OF THE ORGANIZATION OF PERSONNEL TRAINING IN THE MINISTRY OF INTERNAL AFFAIRS OF RUSSIA: ESSENCE, STATE AND DEVELOPMENT TRENDS

The article analyzes the legal norms of federal laws that directly or indirectly regulate the organiza-tion of personnel training for internal affairs bodies (hereinafter – ATS) Of the Russian Federation. Their main provisions are explained, recommendations on the implementation of legislative prescrip-tions in practice are given. Special attention is paid to the interaction and interdependence of the norms of educational legislation and the norms of legislation on service in the Department of Internal Affairs.&#x0D; The analysis made it possible to identify the peculiarities of the organization of personnel training for the replacement of positions of ordinary personnel, junior, middle, senior and senior commanding staff in the Department of Internal Affairs, as well as the legal status (status) cadets of educational organizations of the Ministry of Internal Affairs Of Russia.&#x0D; The work is addressed to police personnel units, educational and research organizations of the Ministry of Internal Affairs Of Russia, centers of professional training of territorial bodies of internal affairs in the subjects of the Russian Federation.
Persistent UAV Formation Flight by Dynamic Agent Replacement and Leader Selection

First responders use Unmanned Aerial Vehicles (UAV) in disaster response to obtain a situational overview. Flying in a formation has the advantage of having multiple airborne sensors and cameras. However, UAV formations are limited in terms of flight time. In this paper we present a system that is capable of achieving persistent formation flight. The system does not need a central coordination unit and is self-organizing by triggering UAVs on the ground to take-off and join the formation whenever a UAV in the formation is about to run out of battery. The replacement process allows the formation to be kept intact for as long as possible. Onboard each UAV, the flight controller handles the formation-keeping and trajectory following while a higher level multi-agent-coordination unit accesses the flight controller to send commands. The leader-follower formation is designed to be rigid and independent of the number of UAVs present in the formation. A prototype consisting of four simulated UAVs was implemented as a proof of concept, which worked as expected.
Modeling the Uncertainty of Concurrent Cyclic Processes

Chapter presents a declarative model of a SCCMP that takes into account fuzzy variables. This model is based on the algebra of ordered fuzzy numbers, which allows avoiding the undesirable effect of the "increasing uncertainty" of results when multiple algebraic operations are performed one after another, i.e., Zadeh's extension principle. This property can be achieved by formulating fuzzy problems of SCCMP behavior analysis and structure synthesis in declarative programming environments.
Entity Relation Extraction for Bridge Inspection Based on Semi-Supervised Learning

To address the issue of poor performance of conventional methods in entity relation extraction in the field of bridge inspection, we propose a bridge inspection field entity relation extraction method called TriC-training based on semi-supervised learning. It utilizes three classification models for collaborative training to achieve better relation extraction results in the bridge inspection field. In this paper, we use three basic classification models, SVM, KNN, and RF. We trained on a bridge inspection field dataset we constructed ourselves, and the experimental results prove that the proposed method has good extraction performance in relation extraction tasks in the bridge inspection field.
Role-oriented representation learning via fusioning local and higher-order feature

Role-oriented representation learning extracts structural features by capturing the structural similarities of nodes to generate representations that preserve the structural equivalence, which is crucial for addressing the role discovery task of real networks. However, most studies directly combine different types of structural features and ignore the correlations among features, which is not enough to accurately portray node roles. Therefore, we propose a novel role-oriented network representation learning method RFLH that mines the correlations among features by normalizing flows to improve the performance on the role discovery tasks. We utilize the Variational Auto-Encoder (VAE) to generate the original representation distribution and learn the structural features of nodes by constraint of local and higher-order features, where local features are learned by the traditional variational module, while higher-order features are learned by the flow-based variational module that introduces normalizing flows to map the original representation distribution to a complex distribution. Moreover, we deduce the new loss by introducing the Jacobian determinant to Evidence Lower Bound (ELBO). Finally, we fuse local and higher-order features into the node representations, which are concatenated by the representations sampled from the original distribution and the new distribution, to generate the final node representations. Extensive experimental results on several real network datasets demonstrate that our model achieves superior performance compared to typical baseline methods.
Disturbing Bit-transition Using History-based Mechanism against Power Analysis Attacks

With the proliferation of IoT edge devices in recent years, there is a growing demand for lightweight tamper-resistant designs. This paper proposes History-based Wave Flip-Flop (HW-FF), a history-based bit-transition disturbing mechanism that improves upon the previous research of Wave Flip-Flop (W-FF) and achieves tamper-resistance comparable to Wave Dynamic Differential Logic (WDDL), which requires significant circuit modifications and additions. In our evaluation, it was confirmed that HW-FF maintains a mere 2% increase in area compared to W-FF in ASIC implementation.
English Listening Teaching Mode under Artificial Intelligence Speech Synthesis Technology

To discuss the application of artificial intelligence (AI) speech synthesis technology and wireless network technology in English listening teaching, firstly, the present situation of English listening teaching is analyzed. Secondly, a questionnaire is designed to investigate the feasibility of AI speech synthesis technology in English listening teaching. Finally, the control group (a traditional mode of English listening teaching) and experimental group (AI speech synthesis technology) are set up, compared, and analyzed. The survey shows that for natural fluency, speech synthesis has the lowest score. The textbook audio score is the highest, with an average score of 4.23. According to the mean opinion score (MOS), the AI speech synthesis technology belongs to high-quality speech coding. Therefore, the English audio generated by combining AI speech synthesis technology under wireless network technology also has high quality. Meanwhile, 113 people in the survey consider that the clear articulation of the synthesized audio is full marks, which is clearer than the English audio matching the textbook, indicating that students think the English audio of the supporting textbook is poor in the clear articulation, which affects students’ listening. Moreover, the English listening scores of the experimental group are notably better than those of the control group. It is hoped that the traditional teaching mode can be better integrated with wireless networks and AI technology, thereby improving the clarity of English listening, providing mobile learning, and advancing students' English performance and learning efficiency.
Romanian Earthquakes Database. Part I: 984 - 1899

"The main purpose of this study is to create a database that correlates the data from the current catalogue of Romanian earthquakes (ROMPLUS) with the information from other available catalogues, research studies for the events produced between 984 and 1899, historical documents, and mentions in archives or historical papers. The database will contain, in addition to the ROMPLUS available information, the type of event (tectonic or anthropic) and the references attached to each event. In the second step of our analysis (events recorded after 1900), the database will be updated and connected with the catalogue of the earthquake mechanisms. "
Bidirectional-Feature-Learning-Based Adversarial Domain Adaptation with Generative Network

Studying domain adaptation is a recent research trend. Generally, many generative models that researchers have studied perform well on training data from a specific domain. However, their ability to be generalized to other domains might be limited. Therefore, a growing body of research has utilized domain adaptation techniques to address the problem of generative models being vulnerable to input from other domains. In this paper, we focused on generative models and representation learning. Generative models have received a lot of attention for their ability to generate various types of data such as images, music, and text. In particular, studies utilizing generative adversarial neural networks (GANs) and autoencoder structures have received a lot of attention. In this paper, we solved the domain adaptation problem by reconstructing real image data using an autoencoder structure. In particular, reconstructed image data, considered a type of noisy image data, are used as input data. How to reconstruct data by extracting features and selectively transforming them in order to reduce differences in characteristics between domains entails representative learning. Considering these research trends, this paper proposed a novel methodology combining bidirectional feature learning and generative networks to innovatively approach the domain adaptation problem. It could improve the adaptation ability by accurately simulating the real data distribution. The experimental results show that the proposed model outperforms the traditional DANN and ADDA. This demonstrates that combining bidirectional feature learning and generative networks is an effective solution in the field of domain adaptation. These results break new ground in the field of domain adaptation. They are expected to provide great inspiration for future research and applications. Finally, through various experiments and evaluations, we verify that the proposed approach outperforms the existing works. We conducted experiments for representative generative models and domain adaptation techniques and found that the proposed approach was effective in improving data and domain robustness. We hope to contribute to the development of domain-adaptive models that are robust to the domain.
A Neural Network for Hyperspectral Image Denoising by Combining Spatial–Spectral Information

Hyperspectral imaging often suffers from various types of noise, including sensor non-uniformity and atmospheric disturbances. Removing multiple types of complex noise in hyperspectral images (HSIs) while preserving high fidelity in spectral dimensions is a challenging task in hyperspectral data processing. Existing methods typically focus on specific types of noise, resulting in limited applicability and an inadequate ability to handle complex noise scenarios. This paper proposes a denoising method based on a network that considers both the spatial structure and spectral differences of noise in an image data cube. The proposed network takes into account the DN value of the current band, as well as the horizontal, vertical, and spectral gradients as inputs. A multi-resolution convolutional module is employed to accurately extract spatial and spectral noise features, which are then aggregated through residual connections at different levels. Finally, the residual mixed noise is approximated. Both simulated and real case studies confirm the effectiveness of the proposed denoising method. In the simulation experiment, the average PSNR value of the denoised results reached 31.47 at a signal-to-noise ratio of 8 dB, and the experimental results on the real data set Indian Pines show that the classification accuracy of the denoised hyperspectral image (HSI) is improved by 16.31% compared to the original noisy version.