Reviewerly/exHarmony huggingface dataset:
Current biased gradiometric flux qubit in a circuit-QED architecture

We propose a scheme for controlling the gradiometric flux qubit (GFQ) by applying an ac bias current in a circuit-QED architecture. The GFQ is insensitive to the magnetic flux fluctuations, which at the same time makes it challenging to manipulate the qubit states by an external magnetic field. In this study, we demonstrate that an ac bias current applied to the $\alpha$-junction of the GFQ can control the qubit states. Further, the present scheme is robust against the charge fluctuation as well as the magnetic flux fluctuations, promising a long coherence time for quantum gate operations. We introduce a circuit-QED architecture to perform the single and two-qubit operations with a sufficiently strong coupling strength.
Path planning method of smart mobile robot based on genetic algorithm

Abstract Robot path planning is an important topic in the field of robots. Unlike previous methods which consider the smoothness of paths in the process of genetic algorithm, this paper presents a new method of robot path planning that separates the genetic algorithm process from the path smoothing process. First, a simple genetic algorithm with variable length encoding is designed to generate a better polyline path, and then a new type of spiral with shape parameters is introduced to smooth it to smooth a larger corner. Throughout the path planning process, only obstacle coordinates are input to select parameters adaptively to generate the robot’s walking path. The simulation results show that separating the genetic algorithm process from the path smoothing process reduces the complexity of the genetic algorithm itself, so the designed smoothing operation not only improves the smoothness of the path, but also reduces the length of the path.
Cloud Technology, Organizational Aspects, and Risks

This chapter provides an overview of cloud computing technology. The explanation includes an overview of the differences between traditional outsourcing and cloud computing and how server virtualization makes cloud computing possible. The chapter also identifies the major players in the provision of cloud computing services and the primary cloud computing service and deployment models. The chapter evaluates central security concerns and risks including loss of availability and risks to data portability.
Trust-based federated learning for network anomaly detection

With the rapid development of social networks and the massive popularity of intelligent mobile terminals, network anomaly detection is becoming increasingly important. In daily work and life, edge nodes store a large number of network local connection data and audit data, which can be used to analyze network abnormal behavior. With the increasingly close network communication, the amount of network connection and other related data collected by each network terminal is increasing. Machine learning has become a classification method to analyze the features of big data in the network. Face to the problems of excessive data and long response time for network anomaly detection, we propose a trust-based Federated learning anomaly detection algorithm. We use the edge nodes to train the local data model, and upload the machine learning parameters to the central node. Meanwhile, according to the performance of edge nodes training, we set different weights to match the processing capacity of each terminal which will obtain faster convergence speed and better attack classification accuracy. The user’s private information will only be processed locally and will not be uploaded to the central server, which can reduce the risk of information disclosure. Finally, we compare the basic federated learning model and TFCNN algorithm on KDD Cup 99 dataset and MNIST dataset. The experimental results show that the TFCNN algorithm can improve accuracy and communication efficiency.
Fast Image Recognition Using Double Hyper Basis Function Neural Network and Its Combined Learning

The proposed neural network with kernel activation functions is designed to solve the image recognition-classification problem in online mode. The modified probabilistic neural network (PNN) as an encoder gets on its input layer image data, represented in matrix form. The outputs of the PNN, a number of which is defined by the amount of classes in a dataset, a vector of signals of pre-classification appears. This signal is fed to the input layer of the hyper basis neural network which reconstructs dividing hypersurfaces between classes of arbitrary complex form. The characteristic property of the system is combined learning: matrix probabilistic neural network is tuned by lazy learning predicated on neurons at data point. Hyper basis neural network is tuned by controlled learning, herein, both synaptic weights and parameters of receptive fields are specified: centers and orientation of hyperellipsoids of activation functions. Along with high classification accuracy, the system is characterized by high learning speed which allows processing data streams that are fed sequentially in online mode.
Bayesian clustering for continuous‐time hidden Markov models

We develop clustering procedures for longitudinal trajectories based on a continuous-time hidden Markov model (CTHMM) and a generalized linear observation model. Specifically in this paper, we carry out finite and infinite mixture model-based clustering for a CTHMM and achieve inference using Markov chain Monte Carlo (MCMC). For a finite mixture model with prior on the number of components, we implement reversible-jump MCMC to facilitate the trans-dimensional move between different number of clusters. For a Dirichlet process mixture model, we utilize restricted Gibbs sampling split-merge proposals to expedite the MCMC algorithm. We employ proposed algorithms to the simulated data as well as a real data example, and the results demonstrate the desired performance of the new sampler.
Analysis of Confucius’s Humanistic Education Theory Centered on the Analects of Confucius Based on Cognitive Anthropology

Objectives: Confucius’s humanistic education theory centered on the Analects of Confucius based on cognitive anthropology was analyzed in this paper, and the computer technology was used to extract the imagery and thought of the ancient Chinese prose. Methods: First of all, the definition and classification of cognitive anthropology and ancient Chinese prose imagery were described in detail; then based on the Confucius culture centered on the Analects of Confucius, the computer representation model and the classification algorithm of ancient Chinese prose were constructed; Results: in addition, the experiment was carried out to verify the model and algorithm, and the threshold analysis was carried out on the basis of the comparison of tagged word and characteristic words; Conclusion: finally, the optimum range was obtained for each parameter.
Typomorphism of Placer Gold and Prerequisites for the Discovery of Gold Ore Occurrences in the Southern Part of the Lena-Anabar Trough (North-East of The Siberian Platform)

Abstract The article examines the results of exploratory work carried out in the southern part of the Lena-Anabar trough in the north-eastern part of the Siberian platform. Alluvial deposits of the Polovinnaya river, which belongs to the Uele-Udzhinsky placer cluster, were sampled for predictive assessment of commercial prospects for placer occurrences of gold. The mineralogical-geochemical features of the placer gold were studied in order to identify potential primary sources. It is proved that the Mesozoic and Cenozoic deposits are the main sources of gold. Its typomorphic features are small size (-0.5 mm), toroidal and flake shape, shagreen surface with casts of pressing of the minerals, and high fineness (950-999 %). The internal structures of gold particles are represented by thick high-grade shells, translation lines and high-grade intergranular veinlets. Along with this, the discovery of the autochtonous gold of ore habit in the studied gold area suggested the presence of an additional primary source. Very small size (-0.2 mm) and angular-lumpy shapes of the individuals, almost unrounded surface and low fineness are typical features of slightly altered gold. The discovery of small (3-4mm) fragments of hydrothermal-metasomatic rocks in the alluvium of the Polovinnaya river confirms the above assumption. The epigenetic mineral composition of these fragments is represented by pyrite, potassium feldspar, fluorapatite, and quartz. Micro-X-ray spectral analysis determined the smallest (first microns) mineral phases of native gold, argentite, galena, sphalerite and greenockite in them. It is worth noting that the described fragments are quite fragile, and they are destroyed with a slight impact with a steel needle. The fragility of these fragments indicates the close proximity of the rewashed ore occurrences. In addition, increased gold content in small classes of heavy concentrate material of small-volume samples was determined according to atomic absorption analysis. All this together is a prerequisite for the discovery of ore occurrences with finely-dispersed and fine gold. It is assumed that the ore occurrences can be localized in the Anabar-Eekit fault zone, which extends parallel to the Pronchishchev ridge, through the Anabar-Olenek trough, to the Sololiy uplift of the Olenek arch. In the area of the Olenek arch, this zone is associated with superimposed gold-bearing low-temperature hydrothermal mineralization, expressed by K-feldspathization and sulfidization of the Permian deposits.
Research on low contrast image enhancement technology

When studying the machine vision of engineering machinery, it is found that the target detection algorithm has a low detection rate when dealing with the photos of night scene. In order to solve this problem, the image is enhanced before entering the neural network. This paper compares the common gray transformation image enhancement methods such as nonlinear transformation, linear transformation, logarithmic transformation and contrast stretching. By adjusting parameters, the four algorithms achieve the best results in dealing with pictures in dark environment. The comparative experimental results show that the image effect using contrast stretching technology is most similar to the image in daily environment. The image processed by contrast stretching can be easily recognized by the target detection algorithm to achieve the research purpose.
Development of an Autonomous Robotic System Using the Graph-based SPLAM Algorithm

For long-term planning, localization and mapping, the robot must constantly update the map by the changing environment and new areas that the robot is exploring. At the same time, this map should not take up too much of the robot’s memory, since the robot’s performance is limited due to the small size of the robot and increased performance requirements. The robot must interact with the map on time, updating its location to build a further route to explore areas that have not been visited. In addition to compiling a map, when solving the problem of exploration rooms, the following steps are also important: forming a plan for bypassing an unknown room, calculating the trajectory, resolving collisions with obstacles, and following the trajectory. In the course of this work, an autonomous robotic system was developed, the task of which is to map previously unknown premises. For this, SPLAM algorithms, algorithms for building map and working with graphs, algorithms for following a trajectory were used.
The deep structure of the Salair fold-cover structure according to magnetotelluric studies

Abstract The results of magnetotelluric studies (MTS) performed within the Salair cover-folded structure on two profiles are considered: the Zabrodino village – the Rodnikovy village (1) and the Smaznevo village – the Kotino village (2). The profiles are oriented crosswise along the main structures and intersect Salair and the western part of the Kuznetskiy trough. The analysis of the obtained data showed that a subhorizontal underlying conducting zone is distinguished in the Earth’s crust of the Salair fold-cover structure, such zone is typical for intracontinental orogens. The zone is considered as a deep separation failure. The nature of the electrical resistance values distribution confirms the presence of the Salair thrust on the Kuznetskiy deflection. The Alambay ophiolite zone on the geoelectric section corresponds to a highly gradient region, indicating the suture zone of this structure. High resistivity values in the northern part of the Khmelevskoy trough are associated with the widespread development of granitoid massifs that are not covered by erosion.
Consolidation Activities of Universities in the Digital Economy

The main purpose is to study the problem of timely adjustment of strategic priorities and strategic planning tasks for the educational organization of higher education. It affects the organization's ability to react quickly in the strategic development and provide the necessary level of competitiveness in the digital economy. For this reason, the priorities of the strategic development of universities are the search for strategic partners and focus on the problems of networking in order to achieve a consolidated result. Networking as a strategic task for pedagogical universities is of paramount importance in the context of financial and resource constraints of both educational organizations of this type and the main stakeholders (network partners, employers). The article summarizes the experience of modern forms of interaction between universities and organizations in various fields of activity; considered options for the formation of partnerships, including the “consortium” format in the digital economy. Possible grounds for the formation of associations and participation in such associations are described. The article presents the results obtained from the analysis of key indicators of pedagogical universities from the point of view of the prospects for their advancement in the program of strategic academic leadership. Based on the results of the study, conclusions were made about the prospects for the participation of pedagogical universities in various associations and promotion in the program of strategic academic leadership. As a result, the conducted assessment allows us to conclude that it is possible to use various forms of interaction between pedagogical universities as promising and priority areas of strategic activity that contribute to the growth of university performance indicators and the effective implementation of their strategy.
Exploring Big Ideas for the Korean Language Curriculum - Based on semantic network analysis of the needs of the school field -

This study explored big ideas for the Korean language curriculum. To this end, expert opinions on big ideas in Korean language subjects were collected and the semantic relationships of major keywords were examined through semantic network analysis. In particular, the opinions of other subject teachers working in field schools were investigated and analyzed. They will be able to objectively convey the demands and expectations of learners and parents on Korean language education without being bound by what has traditionally been emphasized from the perspective of Korean language education insiders. Analysis of the keywords of big ideas in the Korean language revealed the term “language” appeared most often, followed by “human,” “understanding,” “diversity,” “communication,” “expression,” “others,” “self,” and “society” in order of frequency. Analysis of the semantic network revealed the keywords “language,” “understanding,” “human,” “diversity,” “communication,” “expression” had high degree centrality as well as eigenvector centrality and betweenness centrality. They were located at the center of the network and exerted a strong influence within the network. As a result of CONCOR analysis, 78 keywords were categorized into eight clusters. Each cluster formed the following semantic structure: “Importance of language in human life,” “Necessity of learning various communication methods and skills,” “Connecting with others and the world through communication,” “Resolving conflicts through empathic communication,” “Social and cultural influence on text,” “The value of enjoying literature,” “Problem solving through communication,” “Cultivating thinking through language and creating knowledge.”
Graph network for simultaneous learning of forward and inverse physics

In this work, we propose an end-to-end graph network that learns forward and inverse models of particle-based physics using interpretable inductive biases. Physics-informed neural networks are often engineered to solve specific problems through problem-specific regularization and loss functions. Such explicit learning biases the network to learn data specific patterns and may require a change in the loss function or neural network architecture hereby limiting their generalizabiliy. While recent studies have proposed graph networks to study forward dynamics, they rely on particle specific parameters such as mass, etc. to approximate the dynamics of the system. Our graph network is implicitly biased by learning to solve several tasks, thereby sharing representations between tasks in order to learn the forward dynamics as well as infer the probability distribution of unknown particle specific properties. We evaluate our approach on one-step next state prediction tasks across diverse datasets that feature different particle interactions. Our comparison against related data-driven physics learning approaches reveals that our model is able to predict the forward dynamics with at least an order of magnitude higher accuracy. We also show that our approach is able to recover multi-modal probability distributions of unknown physical parameters using orders of magnitude fewer samples.
Quantum annealing in the NISQ era: railway conflict management

We are in the Noisy Intermediate-Scale Quantum (NISQ) devices' era, in which quantum hardware has become available for application in real-world problems. However, demonstrating the usefulness of such NISQ devices are still rare. In this work, we consider a practical railway dispatching problem: delay and conflict management on single-track railway lines. We examine the issue of train dispatching consequences caused by the arrival of an already delayed train to the network segment being considered. This problem is computationally hard and needs to be solved almost in real-time. We introduce a quadratic unconstrained binary optimization (QUBO) model of this problem, compatible with the emerging quantum annealing technology. The model's instances can be executed on present-day quantum annealers. As a proof-of-concept, we solve selected real-life problems from the Polish railway network using D-Wave quantum annealers. As a reference, we also provide solutions calculated with classical methods, including those relevant to the community (linear integer programming) and a sophisticated algorithm based on tensor networks for solving Ising instances. Our preliminary results illustrate the degree of difficulty of real-life railway instances for the current quantum annealing technology. Moreover, our analysis shows that the new generation of quantum annealers (the advantage system) perform much worse on those instances than its predecessor.
Human Interpretation and Exploitation of Self-attention Patterns in  Transformers: A Case Study in Extractive Summarization

The transformer multi-head self-attention mechanism has been thoroughly investigated recently. On one hand, researchers are interested in understanding why and how transformers work. On the other hand, they propose new attention augmentation methods to make transformers more accurate, efficient and interpretable. In this paper, we synergize these two lines of research in a human-in-the-loop pipeline to first find important task-specific attention patterns. Then those patterns are applied, not only to the original model, but also to smaller models, as a human-guided knowledge distillation process. The benefits of our pipeline are demonstrated in a case study with the extractive summarization task. After finding three meaningful attention patterns in the popular BERTSum model, experiments indicate that when we inject such patterns, both the original and the smaller model show improvements in performance and arguably interpretability.
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice  Conversion for everyone

YourTTS brings the power of a multilingual approach to the task of zero-shot multi-speaker TTS. Our method builds upon the VITS model and adds several novel modifications for zero-shot multi-speaker and multilingual training. We achieved state-of-the-art (SOTA) results in zero-shot multi-speaker TTS and results comparable to SOTA in zero-shot voice conversion on the VCTK dataset. Additionally, our approach achieves promising results in a target language with a single-speaker dataset, opening possibilities for zero-shot multi-speaker TTS and zero-shot voice conversion systems in low-resource languages. Finally, it is possible to fine-tune the YourTTS model with less than 1 minute of speech and achieve state-of-the-art results in voice similarity and with reasonable quality. This is important to allow synthesis for speakers with a very different voice or recording characteristics from those seen during training.
Selecting Parallel In-domain Sentences for Neural Machine Translation  Using Monolingual Texts

Continuously-growing data volumes lead to larger generic models. Specific use-cases are usually left out, since generic models tend to perform poorly in domain-specific cases. Our work addresses this gap with a method for selecting in-domain data from generic-domain (parallel text) corpora, for the task of machine translation. The proposed method ranks sentences in parallel general-domain data according to their cosine similarity with a monolingual domain-specific data set. We then select the top K sentences with the highest similarity score to train a new machine translation system tuned to the specific in-domain data. Our experimental results show that models trained on this in-domain data outperform models trained on generic or a mixture of generic and domain data. That is, our method selects high-quality domain-specific training instances at low computational cost and data size.
Modified Selective Kernel Networks and Application in Classification

In standard convolutional neural network, the kernel size or receptive field in each layer is same. It is not the same case with neurons in the primary visual cortex of humans which can be modulated by stimulus. The size of receptive field plays a very important role in recognizing multi-scale objects. In this thesis, our aim is to propose a method which has the ability to change the receptive field size in neurons of the same layer. This is enable the CNNs to detect multi-scale objects efficiently. In the paper, we have described various state of the art architectures which can be used as backbones for our method. We discuss how our model can be used for image search engines ranking.
Research on the Influence of Science and Technology Competition on the Cultivation of Innovation Ability of Engineering College Students

The extracurricular science and technology (S&T) competition, as an important means of cultivating students' innovative ability, has a special educational function, and plays an increasingly positive role in cultivating compound and innovative talents and promoting the combination involving production, teaching and research in colleges and universities.This paper selects some engineering college students who have participated in S&T competitions in Shandong Province as the research object.Through in-depth analysis, this paper studies the influence mechanism of S&T competitions on college students' innovation ability from three aspects of innovation individual, innovation environment and innovation behavior, and obtains the expected results.