Reviewerly/exHarmony huggingface dataset:
Error-insensitive preparation of entangled states between a Josephson qubit and microwave photons via invariant-based shortcuts

Optimal preparation of quantum entanglement is of significance to information processing and state engineering. In this paper, an efficient scheme is proposed to implement error-insensitive generation of entangled states between a Josephson qubit and microwave photons by the technique of invariant-based shortcuts to adiabaticity. A superconducting qubit is dispersively coupled to a quantized cavity field of one-dimensional transmission line resonator. Within a considered subspace spanned by three composite states, we deal with an effective interaction of the composite system with two classical drivings. A maximally entangled qubit-photon state can be deterministically induced using a splitting-like quantum state transfer. To nullify the deviation errors of Rabi coupling and frequency detuning, we optimize the driving parameters and then make the entanglement creation insusceptible to these control imperfections. Thanks to the mitigation of deviation effects, robustness against the residual noisy environment could be obtained numerically. The proposed strategy could provide a promising avenue towards fast and robust information processing with superconducting circuit quantum electrodynamics.
Enhancing Cultural and Linguistic Competence in Korean Education Topic: Korean Food

The purpose of this study is to develop a class lesson plan that integrates Korean culture and language at universities outside of Korea. Under the theme of “Bibimbap”, it was designed by combining Brinton's “Into-Through-Beyond” model, a CBL method, ACTFL 5Cs' “communication,” “culture” elements, and CEFR's “cultural competence. This lesson plan was applied to actual classes which presented a practical example of how this method of teaching can be achieved. As a result of this study, it was found that language and cultural skills improved at the same time. Learners were also found to be interested in all areas that were taught in-class such as language, culture, and presentation activities, which seemed to have a great influence on learners' language and cultural competence. This study is meaningful in that it not only develops a practical teaching model, but it is also applicable to actual classes thus increasing the completeness of the research.
The Integration of Vocal Techniques and Piano Accompaniment in Children's Choir Song Performance under the Background of Information Technology

Vocal singing and piano accompaniment belong to two different forms of artistic expression, each with its own unique charm.However, in the process of vocal singing, by integrating them with piano accompaniment, the two can be in a harmonious state, bringing a more wonderful audio-visual experience to the audience, and achieving many classic music works.The training of children's chorus is the foundation of singing teaching, and it is also an important means to cultivate children's mastery of singing skills and form good vocal literacy.This article elaborates on the issue of harmony and accuracy in children's vocal choir, the characteristics of children's vocal sounds, and several issues that should be paid attention to in training vocal skills.
Deep Learning based Multilingual Speech Synthesis using Multi Feature Fusion Methods

The poor intelligibility and out-of-the-ordinary nature of the traditional concatenation speech synthesis technologies are two major problems. CNN's context deep learning approaches aren't robust enough for sensitive speech synthesis. Our suggested approach may satisfy such needs and modify the complexities of voice synthesis. The suggested model's minimal aperiodic distortion makes it an excellent candidate for a communication recognition model. Our suggested method is as close to human speech as possible, despite the fact that speech synthesis has a number of audible flaws. Additionally, there is excellent hard work to be done in incorporating sentiment analysis into text categorization using natural language processing. The intensity of feeling varies greatly from nation to country. To improve their voice synthesis outputs, models need to include more and more concealed layers &amp; nodes into the updated mixture density network. For our suggested algorithm to perform at its best, we need a more robust network foundation and optimization methods. We hope that after reading this article and trying out the example data provided, both experienced researchers and those just starting out would have a better grasp of the steps involved in creating a deep learning approach. Overcoming fitting issues with less data in training, the model is making progress. More space is needed to hold the input parameters in the DL-based method.
Machine Learning Explicit and Implicit Model for Factor Classification Based on Factor Space Theory

Factor implicit thought in factor space theory can be used to solve the classification problem of machine learning. On the basis of factor implicitness, a serial sweeping class classification algorithm is proposed, and a factor implicit model is constructed with this algorithm, and then tested and classified. On the basis of a serial scanning algorithm, this paper proposes a fine-tuning Sweeping Learning Algorithm, an ascending Side-By-Side Serial Scanning Algorithm, and a combination algorithm to solve the two problems in the running of the serial scanning algorithm. In order to solve the problem that the training speed of a traditional serial scanning algorithm is too slow due to the large amount of data, a new method which can select mixed domains in advance—the partial-side serial scanning algorithm.
Scientific-Linguistic Framework: Identification of Tonal Relations

This study establishes a scientific-linguistic framework to specify tonal relations in Asian tonal languages. Applied to the empirical data from 21 speakers of Zhangzhou Southern Min, this model derives a linguistically tonetic F0 system for its eight-way tonal contrast. This represents this dialect as an independent variety from the scientific perspective while making it comparable with other tonal varieties and languages for typological and universal purposes. The description has gone beyond the inconsistency and inadequacy of those auditorily-based prior studies and laid out an objective foundation for further theoretical exploration. The exploration is expected to serve as a model to examining those tonal languages that possess complex tonal contrasts not only in China but also in Asia and beyond. It contributes vital linguistic data to the generalization of areal characteristics within the Sino-Tibetan language family, but also to the exploration of cross-linguistic tonal property in world’s natural languages, while enlightening the discussion on why and how human beings employ tone as an important language device in their cognitive activities and decode the competence for communicative purposes.
OPTIMIZATION OF THE QUALITY OF ELECTRICITY WITH INFORMATION UNCERTAINTY

The concept of forming an objective function for solving the problem of the quality of electrical energy optimizing in power networks is proposed, which takes into account the uncertainty factor of information about the mode parameters. This will allow, in contrast to the traditional binary function of assessing the quality of electrical energy for compliance with the established standards, to take into account the dynamics of changes in the efficiency criterion and perform the search for optimal values of the relevant controlling influences. The classification of information flows about the mode of the electric network is presented and the boundaries of fuzzy set for the values of the mode parameters are defined. It is shown that the objective function should be appropriately formed on the basis of fuzzy logic. The principles of the formation of fuzzy mathematical models for the analysis of established normal modes of the electrical network are defined. It is proposed to determine the membership function of a fuzzy set taking into account the quality of information that can be provided by the technical means of the electrical network. Ref. 5, fig. 3. Keywords: electrical network, quality of electrical energy, uncertainty of information, fuzzy mathematical model.
Simulating noise on a quantum processor: interactions between a qubit and resonant two-level system bath

Abstract Material defects fundamentally limit the coherence times of superconducting qubits, and manufacturing completely defect-free devices is not yet possible. Therefore, understanding the interactions between defects and a qubit in a real quantum processor design is essential. We build a model that incorporates the standard tunneling model, the electric field distributions in the qubit, and open quantum system dynamics, and draws from the current understanding of two-level system (TLS) theory. Specifically, we start with one million TLSs distributed on the surface of a qubit and pick the 200 systems that are most strongly coupled to the qubit. We then perform a full Lindbladian simulation that explicitly includes the coherent coupling between the qubit and the TLS bath to model the time dependent density matrix of resonant TLS defects and the qubit. We find that the 200 most strongly coupled TLSs can accurately describe the qubit energy relaxation time. This work confirms that resonant TLSs located in areas where the electric field is strong can significantly affect the qubit relaxation time, even if they are located far from the Josephson junction (JJ). Similarly, a strongly-coupled resonant TLS located in the JJ does not guarantee a reduced qubit relaxation time if a more strongly coupled TLS is far from the JJ. In addition to the coupling strengths between TLSs and the qubit, the model predicts that the geometry of the device and the TLS relaxation time play a significant role in qubit dynamics. Our work can provide guidance for future quantum processor designs with improved qubit coherence times.
Hate Crime Investigation and Sentencing in Sweden: What Have We Learned in the Past 20 Years?

Abstract Twenty years ago, the Swedish National Council for Crime Prevention presented a report that highlighted serious problems with regard to identifying, investigating, and sentencing offenders for hate crimes. The same problems have also been described in international research from several other countries. Since then, several measures have been taken to remedy these problems, but it remains unknown whether these measures have been successful. The aim of the present study is therefore to trace developments over time, using Sweden as a case study, and to evaluate the extent to which the problems identified earlier have been remedied. The results show that the problems identified by the Swedish National Council for Crime Prevention still remain despite a continuous process of reform. Theoretical links and parallels to international research are discussed throughout the article.
An Efficient Medical Image Encryption Scheme Utilizing Nonuniform Cellular Automaton

Encryption is one of the simplest and most efficient technologies to protect images from information leakage. Because medical images usually have great data volume and personal privacy, encryption methods with higher efficiency and security are expected. The nonuniform cellular automaton (CA), which has common advantages of CA such as high efficiency and discreteness, has a unique advantage in handling inputs with all zero pixels, and thus emerges as a promising candidate for medical image encryption. In this work, our proposal adopts the permutation–diffusion–diffusion structure rather than the classic permutation–diffusion structure to improve the efficiency and security. In the diffusion phase, the reversible nonuniform CA is adopted to encrypt the processed pixels, which can improve security against plaintext attacks. An efficient “divide-and-rule” diffusion method is designed for better diffusion performance and efficiency. The security and robustness analysis indicates that our method has satisfactory security against common attacks and robustness in resisting data loss and random noise.
The integration of traditional culture and educational drama for young children based on the AdaBoost model

Abstract However, the integration of traditional culture and modern children’s theater education is not smooth in digitalizing big data information. Take shadow puppet theater. Its traditional culture is no longer as brilliant as yesterday due to the problems of not easy preservation and disconnected content. This paper focuses on integrating traditional culture and educational drama for young children based on the AdaBoost model. Firstly, we understand the principle, concept, and algorithm of the AdaBoost model by finding literature, constructing the AdaBoost-BPN prediction model and AdaBoost-WBLS model, and adopting ordinary BPN weak predictor, AdaBoost-BPN The AdaBoost-BPN prediction model and the AdaBoost-BPN prediction model with “weekend factor” were used to predict the traditional culture and educational drama of young children, and the prediction error evaluated the prediction results. Has higher integration training efficiency. In the integration weight update, different regularization update methods are used for different data categories to retain the features of the data and increase the inter-class distinction. WBLS is used as the base classifier and the optimization method of embedding WBLS in the AdaBoost framework to improve the performance of the integration of early childhood traditional text and educational drama. The results show that the AdaBoost-WBLS model has significantly improved G-mean on dichotomous and multiclassified datasets compared with similar models, with higher accuracy and better ability to handle unbalanced data. This study is conducive to planning the design of the teaching process according to the characteristics of the integration and change of traditional culture and educational drama for young children, and the combination of traditional culture represented by shadow puppet theater and children’s drama education is of great significance and value in guiding young children to establish the correct three views, stimulating their imagination and creativity, and promoting the development of their aesthetic education.
The impact of CS structured computer-assisted translation system on English linguistics teaching in universities

Abstract In the age of information technology, the teacher is no longer the only source of information, and a single lecture mode can trigger a range of messages for learning emotions. For this reason, this paper designs a cs-structured computer-aided translation system composed of machine translation in a neural network. After solving the exposure bias problem, the model regularization method combined with the bi-directional decoding consistency is used to optimize the assisted translation system. Then, aspects of the effectiveness of students’ use of the system on their English proficiency improvement were studied. The test scores of the test and comparison classes showed that the mean values of the scores before and after the test of the test class were 13.58 and 15.94, with a mean difference of 2.36 points. The mean values of the scores before and after the test of the comparison class were 14.58 and 14.94, respectively, with a mean difference of 0.4. Comparing the absolute values of the mean differences between the scores before and after the test of the two classes, it is clear that the test class improved their English proficiency level significantly more than the comparison class. Furthermore, the overall satisfaction of the teachers using the system reached 85.62%. Therefore, in terms of traditional teaching methods, the assisted translation system is more capable of improving students’ English proficiency. It enables teachers to improve English teaching efficiency in the classroom and promotes the modernization and intelligence of English teaching.
Research on the optimization and practice of mathematical modeling curriculum systems based on the perspective of big data

Abstract Today’s society is undergoing rapid changes from the information age to the big data age and the intelligence age, and some new requirements and challenges are faced by mathematical modeling curriculum teaching in talent training in the big data age. In this paper, we propose a self-adaptive genetic algorithm scheme for the optimization of a mathematical modeling curriculum system, which is mainly reflected in re-planning and designing the constraints for the optimization of the mathematical modeling curriculum, optimizing and improving the initialization population, selection operator, crossover operator, and variation operator processes. The crossover probability at 0.8, the optimal value of individual fitness and the average value of fitness of the group are both maximum, and the variation probability at 0.01, the optimal value of individual fitness and the average value of fitness of mathematical modeling are both maximum, which indicates that this crossover probability and variation probability are better in the quality of the self-adaptive genetic algorithm. This Study is a guiding reference for the research on the optimization and practical exploration of mathematical modeling curriculum systems in colleges and universities and thus is of historical importance to promote the development of Chinese mathematics.
Optimizing Strategy of Computer Teaching Mode in Universities Based on Continuous Discrete Method

Abstract To improve the teaching effect of computer courses, make students master computer knowledge, and improve computer operation skills, this paper proposes a computer teaching model of continuous, discrete methods in colleges and universities. This paper investigates the teaching mode of traditional computer teaching methods in colleges and universities and proposes a continuous, discrete computer teaching model for the drawbacks of traditional computer teaching methods. The discrete learning teaching matrix and teaching process are constructed using the information entropy-based rough set discretization algorithm. Moreover, the teaching effects are compared with traditional methods regarding students’ learning interests, homework completion, and test scores. In terms of homework completion rate: the correct homework rate of the class using the discrete learning method was 20% higher than that of the class using the traditional teaching method. Regarding skill and overall test scores, the number of students who excelled in the classes using discrete learning reached about 50%, 20% more than the conventional teaching method. 20% fewer students scored below 60 than in the classes using the conventional teaching method. Moreover, after implementing discrete learning concept teaching, students’ interest in learning increased by more than 40%. Therefore, it can be seen that the teaching mode of the discrete learning concept is beneficial to the teaching of computer courses.
Esearch on the optimization path of campus football teaching based on deep learning mode

Abstract To better improve students’ physical quality, this paper constructs a self-coding training model for the optimal path of football physical education based on the self-coding neural network in the deep learning model. The student’s physical performance and teaching methods are input into the self-coding neural network for the optimal path of football physical education as the input layer. The data are corrected by regularizing and balancing the data in the input layer through coding and decoding in the implicit layer. The corrected data is reconstructed and transformed as the input layer of the next level of the self-coding neural network. The above steps are repeated until the same output layer parameters as the pre-trained model are reached, resulting in the optimization path of football physical education teaching: changing the teaching mode and improving the teaching ability of teachers. The simulation results show that the optimized teaching mode can improve the average score of students’ football physical education by 33 points. Taking junior students as an example, after the teacher’s teaching ability was improved, the excellent rate of students’ football physical education scores increased from 7% to 29%. From the above results, it can be seen that the optimized path of football physical education based on the deep learning model is feasible and can improve students’ physical quality.
Practical Quantum Optics

This short course aims at giving an introduction to quantum optics using phenomenological approaches and visualizing the field from the viewpoint of a practitioner. There are altogether five parts: (1)pointing out a close link between Maxwell's equations and the properties of the quantum vacuum with its virtual particle anti-particle pairs [1], [2]; (2)introduction to field quantization from an experimental perspective using the intuition of a classical optical scientist; (3)answering the question “What does it mean if optics is quantum? What distinguishes quantum from classical? [3], [5] (4)description of quantum optical experiments with discrete and with continuous variables with special emphasis on Wigner functions in phase space and the special role of the quantum measurement process [4]; (5)In this fifth part we address the question: “Is hard core quantum optics solely concerned with the study of fundamental physics questions or is it also useful for practical applications? From laser interferometric gravitational wave detection to quantum distribution of cryptographic keys and quantum information processing [6]–[8].
Entanglement Source for Space-Ground Quantum Communication

We present a high rate entangled-based photon source working at telecom wavelength, com-patible with existing fibre communication networks, with hybrid encoding (energy-time and polarisation), with the aim of fulfilling spatial constraints. © 2023 The Author(s)
Integrated Photonics for Quantum Communication on a Cubesat

The limited range of quantum key distribution (QKD) in fiber based systems led to several projects aiming for the development of a satellite based QKD infrastructure, like the conduction of quantum limited measurements from a geostationary satellite [1] or the MICIUS mission, which impressively demonstrated QKD on a global scale [2]. However, due to the high costs of satellite launches the profitability of commercial systems would benefit dramatically from further reduction in size, weight and power. Photonic integration [3] addresses these points by allowing the integration of all optical components of a QKD system on a photonic integrated circuit (PIC). In this work, we are going to demonstrate building blocks for quantum communication from space based on photonic integration technology by building a payload for the CubeSat mission QUBE [4] consisting of an integrated sender for weak coherent states as well as an integrated Quantum Random Number Generator (QRNG).