Reviewerly/exHarmony huggingface dataset:
The Effect of GeoGebra Software on Achievement and Engagement Among Secondary School Students

GeoGebra software has been identified as a potential aid in the teaching and learning process. This study aims to examine the impact of GeoGebra software on achievement and enjoyment in the context of the polygon topic. A non-equivalent group pretest-posttest design was utilized, and 60 students were randomly assigned to either the experimental or control group. The data collected was analyzed using descriptive and inferential statistical methods with the assistance of SPSS version 26. The findings revealed that the level of achievement and engagement among Form Two students in the study population was already high regarding the Polygon topic. However, the results demonstrated that students who received instruction utilizing GeoGebra software achieved even higher levels of achievement and engagement than those who experienced traditional teaching methods. As a result, integrating GeoGebra software into the classroom proved to be an effective tool for enhancing student achievement and engagement on the Polygon topic among Form Two students. Finally, we found a significant relationship between the experience of enjoyment and the academic performance of second-year students.
Applications of Entanglement: Heisenberg-Limited Interferometry and Quantum Information Processing

This established textbook provides an accessible but comprehensive introduction to the quantum nature of light and its interaction with matter. The field of quantum optics is covered with clarity and depth, from the underlying theoretical framework of field quantization, atom–field interactions, and quantum coherence theory, to important and modern applications at the forefront of current research such as quantum interferometry, squeezed light, quantum entanglement, cavity quantum electrodynamics, laser-cooled trapped ions, and quantum information processing. The text is suitable for advanced undergraduate and graduate students and would be an ideal main text for a course on quantum optics. This long-awaited second edition builds upon the success of the first edition, including many new developments in the field, particularly in the area of quantum state engineering. Additional homework problems have been added, and content from the first edition has been updated and clarified throughout.
Quantum Coherence Functions

This established textbook provides an accessible but comprehensive introduction to the quantum nature of light and its interaction with matter. The field of quantum optics is covered with clarity and depth, from the underlying theoretical framework of field quantization, atom–field interactions, and quantum coherence theory, to important and modern applications at the forefront of current research such as quantum interferometry, squeezed light, quantum entanglement, cavity quantum electrodynamics, laser-cooled trapped ions, and quantum information processing. The text is suitable for advanced undergraduate and graduate students and would be an ideal main text for a course on quantum optics. This long-awaited second edition builds upon the success of the first edition, including many new developments in the field, particularly in the area of quantum state engineering. Additional homework problems have been added, and content from the first edition has been updated and clarified throughout.
Experiments in Cavity QED and with Trapped Ions

This established textbook provides an accessible but comprehensive introduction to the quantum nature of light and its interaction with matter. The field of quantum optics is covered with clarity and depth, from the underlying theoretical framework of field quantization, atom–field interactions, and quantum coherence theory, to important and modern applications at the forefront of current research such as quantum interferometry, squeezed light, quantum entanglement, cavity quantum electrodynamics, laser-cooled trapped ions, and quantum information processing. The text is suitable for advanced undergraduate and graduate students and would be an ideal main text for a course on quantum optics. This long-awaited second edition builds upon the success of the first edition, including many new developments in the field, particularly in the area of quantum state engineering. Additional homework problems have been added, and content from the first edition has been updated and clarified throughout.
Emission and Absorption of Radiation by Atoms

This established textbook provides an accessible but comprehensive introduction to the quantum nature of light and its interaction with matter. The field of quantum optics is covered with clarity and depth, from the underlying theoretical framework of field quantization, atom–field interactions, and quantum coherence theory, to important and modern applications at the forefront of current research such as quantum interferometry, squeezed light, quantum entanglement, cavity quantum electrodynamics, laser-cooled trapped ions, and quantum information processing. The text is suitable for advanced undergraduate and graduate students and would be an ideal main text for a course on quantum optics. This long-awaited second edition builds upon the success of the first edition, including many new developments in the field, particularly in the area of quantum state engineering. Additional homework problems have been added, and content from the first edition has been updated and clarified throughout.
Introduction

This established textbook provides an accessible but comprehensive introduction to the quantum nature of light and its interaction with matter. The field of quantum optics is covered with clarity and depth, from the underlying theoretical framework of field quantization, atom–field interactions, and quantum coherence theory, to important and modern applications at the forefront of current research such as quantum interferometry, squeezed light, quantum entanglement, cavity quantum electrodynamics, laser-cooled trapped ions, and quantum information processing. The text is suitable for advanced undergraduate and graduate students and would be an ideal main text for a course on quantum optics. This long-awaited second edition builds upon the success of the first edition, including many new developments in the field, particularly in the area of quantum state engineering. Additional homework problems have been added, and content from the first edition has been updated and clarified throughout.
Nonclassical Light

This established textbook provides an accessible but comprehensive introduction to the quantum nature of light and its interaction with matter. The field of quantum optics is covered with clarity and depth, from the underlying theoretical framework of field quantization, atom–field interactions, and quantum coherence theory, to important and modern applications at the forefront of current research such as quantum interferometry, squeezed light, quantum entanglement, cavity quantum electrodynamics, laser-cooled trapped ions, and quantum information processing. The text is suitable for advanced undergraduate and graduate students and would be an ideal main text for a course on quantum optics. This long-awaited second edition builds upon the success of the first edition, including many new developments in the field, particularly in the area of quantum state engineering. Additional homework problems have been added, and content from the first edition has been updated and clarified throughout.
Coherent States

This established textbook provides an accessible but comprehensive introduction to the quantum nature of light and its interaction with matter. The field of quantum optics is covered with clarity and depth, from the underlying theoretical framework of field quantization, atom–field interactions, and quantum coherence theory, to important and modern applications at the forefront of current research such as quantum interferometry, squeezed light, quantum entanglement, cavity quantum electrodynamics, laser-cooled trapped ions, and quantum information processing. The text is suitable for advanced undergraduate and graduate students and would be an ideal main text for a course on quantum optics. This long-awaited second edition builds upon the success of the first edition, including many new developments in the field, particularly in the area of quantum state engineering. Additional homework problems have been added, and content from the first edition has been updated and clarified throughout.
Generating images of the M87* Black Hole using GANs

ABSTRACT In this paper, we introduce a novel data augmentation methodology based on Conditional Progressive Generative Adversarial Networks (CPGAN) to generate diverse black hole (BH) images, accounting for variations in spin and electron temperature prescriptions. These generated images are valuable resources for training deep learning algorithms to accurately estimate black hole parameters from observational data. Our model can generate BH images for any spin value within the range of [−1, 1], given an electron temperature distribution. To validate the effectiveness of our approach, we employ a convolutional neural network to predict the BH spin using both the GRMHD images and the images generated by our proposed model. Our results demonstrate a significant performance improvement when training is conducted with the augmented data set while testing is performed using GRMHD simulated data, as indicated by the high R2 score. Consequently, we propose that GANs can be employed as cost-effective models for black hole image generation and reliably augment training data sets for other parametrization algorithms.
Graph Density and Uncertainty of Graphical Model Selection Algorithms

Graphical models became a popular tool in machine learning and data analysis. Graphical Model Selection Problem is a problem to recover a specific graph structure (graphical model) from a set of data. In many cases the data are given by a sample of observations of some multivariate distribution. In this setting any reconstruction algorithm can be evaluated by uncertainty of identification of the hidden graphical model by observations. In the present paper we study uncertainty of identification of so-called concentration graph which represents a dependence structure for the components of multidimensional random vector. We introduce and discuss different measures of uncertainty appropriate for the concentration graph identification problem and compare on this basis different identification algorithms, including optimization (graphical lasso) algorithm and a family of known multiple hypotheses testing algorithms. Novelty of our approach is in the study of dependence of uncertainty on the graph density. Some new and interesting phenomena are observed and discussed.
LSTM Based Forecasting and Evaluation of Sensor Signals Containing Anomaly Data

Sensors are being used in various fields such as smart factories, CPS, and digital twins. It is important to minimize and prevent losses by forecasting abnormal sensor signals when these sensors malfunction or when abnormal signals occur due to environmental effects. Recently, various studies have been conducted with LSTM, which is widely used as time series prediction, but there are not many studies that evaluate performance by applying this to sensor signals. In this study, an LSTM with excellent performance was designed with an optimal hyperparameter setting by applying a water distribution sensor containing 7-17% abnormal sensor data. In addition, as a result of performance comparison with ARIMA, it was found that LSTM was 94.29% superior on average in terms of precision and accuracy.
Discrete Tomography Problems with Paired Projections and Complexity Characteristics

This paper presents a study of discrete tomography problems with horizontal, vertical, and paired projections, where paired projections reflect the conditions of generalized degree sequences of hypergraphs. Paired projections, as far as we know, are considered for the first time in the problems of discrete tomography. The general purpose of introducing these projections is to involve more information about the object under consideration in order to narrow the class of all possible solutions when a solution exists. On the other hand, the consideration of pairwise projections successfully complements our previous research, which is devoted to the study of structures with different classes. In this paper, we consider different types of pairwise projections, including the set of all pairs of columns, the set of all pairs with one fixed column, and the set of all pairs of neighboring columns; and investigate the complexity characteristics of the corresponding problems.
Implications of Blockchain Technology to Sustainable Supply Chain Management

In the contemporary global economy, businesses depend on dynamic Supply Chains (SCs) that span various industries. The efficient management of information flows and stakeholders’ relations is required to achieve a sustainable and responsive SC. The integration of Blockchain Technology (BT) revolutionizes SC operations by providing transparent and real-time information sharing. This enables managers to effectively monitor SC activities with the aim of promoting responsible sourcing and addressing environmental concerns. Despite its potential, the BT adoption frameworks still require further improvement to facilitate the successful implementation of this technology in SCs. This study presents a comprehensive overview of the characteristics of BT that contribute to achieving sustainability in SCs, as well as the potential challenges associated with the adoption of BT.
On Rényi Differential Privacy in Statistics-based Synthetic Data Generation

Privacy protection with synthetic data generation often uses differentially private statistics and model parameters to quantitatively express theoretical security. However, these methods do not take into account privacy protection due to the randomness of data generation. In this paper, we theoretically evaluate Rényi differential privacy of the randomness in data generation of a synthetic data generation method that uses the mean vector and the covariance matrix of an original dataset. Specifically, for a fixed α > 1, we show the condition of ϵ such that the synthetic data generation satisfies (α, ϵ)-Rényi differential privacy under a bounded neighboring condition and an unbounded neighboring condition, respectively. In particular, under the unbounded condition, when the size of the original dataset and synthetic dataset is 10 million, the mechanism satisfies (4, 0.576)-Rényi differential privacy. We also show that when we translate it into the traditional (ϵ, δ)-differential privacy, the mechanism satisfies (4.46, 10-14)-differential privacy.
The Law Applicable to (Digital) Transfer of Digital Assets: The Transfer of Cryptocurrencies via Blockchains

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Microseismic sensor placement optimization for rapid leakage alarm of geologic carbon storage

Effective monitoring of geological carbon storage requires the implementation of microseismic monitoring, which relies on a surface geophone network to promptly detect microseismic events caused by CO2 injection. Recent research has identified the optimal number of seismic stations for a regular seismic network that meets budget constraints and enhances leak source localization capabilities. To develop a cost-effective monitoring strategy, we introduce a mathematical method for determining the optimal placement of sensors to enable the earliest possible alarm for microseismic events. This method minimizes the detection time and improves monitoring performance.
Gated based quantum computer algorithm for geophysical inversion

We want to show a framework to solve geophysical inversion problems using a gated based quantum computer. The resultant quantum algorithm belongs to the quantum search algorithm class where the query is a forward modelling calculation, and we look for the minimum residual. This quantum algorithm can find the global minimum in O(pN) queries where N is the number of models. This algorithm does not need gradient or Hessian calculations, only forward modelling, and residuals calculation. We illustrate the algorithm with a small instance of a traveltime tomography problem.
Twin Support Vector Regression with Privileged Information

In this paper, we propose a novel framework called Twin Support Vector Regression with Privileged Information (TSVR+), which aims to improve the predictive performance of twin support vector regression by incorporating additional privileged information during the learning process. This TSVR+ introduces a twin-learning strategy that utilizes both original and privileged features to construct two non-parallel boundary functions. One for positive deviations and the other for negative deviations and each boundary function is associated with a different set of support vectors. Then, by solving two smaller quadratic programming problems, this proposed method achieves faster learning and better prediction performance. Notably, this twin-boundary approach equipped with privileged information provides a more robust representation of the relationship between input features and target values, allowing for better modeling of complex regression problems. Experimental results on various datasets demonstrate the effectiveness of our approach compared to other regression methods.
Dataset Search over Integrated Metadata from China’s Public Data Open Platforms

The aggregation, fusion, sharing, opening, development, and utilization of public data provide a solid basis for promoting the development of e-government and digital economy. These activities rely on an infrastructure software called public data open platform (PDOP) to provide enabling services. While China’s national PDOP has yet to be completed, one pathway is to integrate the existing hundreds of provincial-level and prefectural-level PDOPs. However, these local PDOPs exhibit high heterogeneity, e.g., using different data catalogs and different metadata formats. In this system paper, we meet the challenge by crawling and integrating metadata records for datasets registered in existing local PDOPs, and we develop a prototype PDOP that provides unified search services over the integrated metadata. We conduct experiments to evaluate the core components of our prototype.
Detecting Social Robots Based on Multi-view Graph Transformer

With the development of social media, social robots are increasingly interfering in political elections and economic issues, and detecting social robots has become a long-standing but unresolved problem. Traditional machine learning models or methods have gradually become ineffective with the evolution of social robots. In contrast, analyzing the social network structure where social robots are located has become a more effective method. However, at present, most of these methods are based on a single view, ignoring the importance of multi-view information in social networks. At the same time, existing methods have not considered the influence of potential topic structures. Therefore, to solve the above problems, this paper proposes a new framework based on multi-view (Multi-View Graph Transformer, MV-GT). Specifically, MV-GT includes a topic module, a graph enhancement module, a multi-view Graph Transformer module, and a multi-view attention module, which can explore the complementarity, consistency, and semantic relevance of multiple different views in online social networks. Experimental results show that MV-GT outperforms many existing methods and also demonstrates the effectiveness of multi-view and topic structures in detecting social robots.
Multimodal emotion recognition based on long-distance modeling and multi-source data fusion

With the prevalence of social media, people are increasingly inclined to express emotions in various forms such as text, images, audio, and video, which has made emotion recognition tasks more complex and challenging. Although traditional deep learning algorithms such as CNN and RNN have been widely used in multimodal emotion recognition, there are still challenges in fusing multimodal features and handling long-distance dependencies which affect the accuracy of emotion classification. To address these issues, this paper proposes a multimodal emotion recognition model based on long-distance modeling and multi-source data fusion. The model uses Transformer to encode concatenated multimodal feature vectors, and uses multi-head attention mechanism for feature fusion. It also introduces cross-layer residual connections to enhance the model's generalization ability. Finally, a multilayer perceptron is used to learn the complex relationship between feature vectors and achieve more accurate emotion classification results. The results on the IEMOCAP dataset show that the model's average accuracy reaches 59.3% and F1 value reaches 56.8%. Compared with the baseline methods, the proposed model improves the accuracy, demonstrating its effectiveness in emotion recognition tasks.
Lightweight graph convolutional networks combining multimodal fusion and spatio-temporal attention mechanisms

Since most mainstream human action recognition methods suffer from high operational complexity and large network parameters, and skeleton behavior recognition is inaccurate for spatio-temporal feature extraction and difficult to capture global contextual information. Firstly, in the data pre-processing stage, three kinds of information data streams related to nodes and skeleton are fused so as to reduce the number of network parameters and improve the operation speed; secondly, the recognition accuracy is improved by constructing a spatio-temporal attention module based on non-local operations to focus on nodes with action discriminative; then a spatio-temporal feature extraction module is constructed to obtain spatio-temporal association information; finally, the Softmax layer is used for action The final action recognition is performed by the Softmax layer. The algorithm achieves accuracies of 89.4% and 95.5% for the two evaluation metrics on the NTU60 RGB+D dataset, and 82.4% and 82.8% on the NTU120 RGB+D dataset. Finally, we did a comparison test with other methods on the dataset UCF101, and the results showed that the recognition speed and accuracy were improved, reflecting the superiority and reliability of the method.
An optimized neural network approach and its application to classification problems

Traditional neural network models are plagued with slow training speed and low classification accuracy, posing numerous challenges to the development of image classification technology. To address these issues, this paper employs a multi-strategy integrated optimization framework to optimize the training speed and classification accuracy of the model on the basis of the Resnet-18 network model. Firstly, the center loss function is introduced to optimize the softmax loss function, thereby improving the classification accuracy. Secondly, the original SGD optimizer is replaced with the Adam optimizer to address the slow training speed of the Resnet-18 network model. A garbage dataset is utilized to validate the effectiveness and feasibility of the proposed algorithm. The results demonstrate that after 40 rounds of training, the classification accuracy of the model optimized by multiple strategies increased from 81.42% to 87.42%. The neural network model optimized by multiple strategies can effectively identify classification problems, thereby enhancing the accuracy and stability of the model.