Reviewerly/exHarmony huggingface dataset:
Construction of a Japanese-Chinese Bilingual Corpus for Learning Japanese Sentence Patterns through Multi-Level Annotation

This work introduces the construction of a Japanese-Chinese bilingual corpus, aiming to help Chinese-speaking learners of Japanese as a second language (JSL) with their study of Japanese sentence patterns in Japanese grammar. For constructing this Japanese-Chinese bilingual corpus, methods of automatic identification of the Japanese sentence patterns were proposed by annotating a dataset on multiple levels for training a model based on Conditional Random Fields (CRF) machine learning algorithm. The multi-level annotation performed is Part-of-speech (POS) tagging, which includes the detailed information, such as surface form, dictionary form, pronunciation, difficulty level and etc. The well-trained model is applied to automatic identification and suggestion of the Japanese sentence patterns with different difficulty levels according to Japanese Language Proficiency Test (JLPT). We evaluated the model on a test dataset in order to show the effectiveness of our proposed methods. We also show how we used this model to construct a Japanese-Chinese bilingual corpus, and manually corrected the example sentences where the Japanese sentence patterns were identified incorrectly or not identified. This model can be effectively used for the Japanese language processing framework, which allows for a fast and easy application for the problem of Japanese grammar extraction. Moreover, the constructed Japanese-Chinese bilingual corpus can be applied as a useful computer-assisted language learning (CALL) tool for learning Japanese sentence patterns.
Research on Tibetan Speech Synthesis Based on Fastspeech2

As the core technology of human-computer interaction, speech synthesis plays an important role in education and life, science technology. Especially as a research hotspot in the field of artificial intelligence, speech synthesis has not only achieved extraordinary results in Mandarin but also in minority languages such as Tibetan got good results. At present, Tibetan speech synthesis research is mainly based on autoregressive models, which are far superior to traditional models and can synthesize high-quality speech. However, due to the slow inference speed of the autoregressive model and the implicit features of the speech duration alignment, pitch, and energy of the acoustic model, there are problems such as slow synthesis speed, repeated words or word skipping, and the inability to control speech rate and prosody in a fine-grained manner. In response to the above problems, this paper studies Tibetan text-to-speech alignment and Tibetan speech synthesis based on a combination of a non-autoregressive acoustic model and vocoder. First, Tibetan speech and phoneme alignment are performed based on the Hidden Markov Gaussian Mixture alignment model. Secondly, the phoneme duration of real speech combined with variable information such as pitch and energy is introduced into the Fastspeech2 acoustic model, and the Variance Adapter is used to solve the one-to-many problem of traditional speech synthesis, reducing word skipping and repetition. Finally, to take into account both synthesis speed and synthesis quality, a pre-trained HiFi-GAN vocoder is used to convert the mel spectrum to speech.
Defense-CycleGAN: A Defense Mechanism Against Adversarial Attacks Using CycleGAN to Reconstruct Clean Images

Deep Neural Networks (DNNs) approaches have been used successfully in various computer vision tasks. However, they are particularly susceptible to adversarial attacks, which can cause incorrect predictions and raise security risk to real-world deep learning applications, e.g., autonomous driving and surveillance systems. Many state-of-the-art adversarial defense models use Variational Autoencoder Decoder (VAE) to reconstruct clean images to counter adversarial attacks. However, images reconstructed from VAEs are often blurry, thus, not only their use for classification is limited, but also such defense methods blur the benign inputs and impair the accuracy when there is no adversarial attack. We propose to use Cycle Consistency GAN (CycleGAN) as the image reconstruction block, which generates higher quality images for better classification performance. Our method achieves 5%-13% higher accuracy than the best VAE-based defense models on CIFAR10 and achieves 90%-98% accuracy for Fashion-MNIST across wide range of adversarial attacks.
Dynamic Symmetry-Breaking and Transverse Photo Response

A transverse photoresponse to a dynamic symmetry-breaking by an external current is investigated in a structurally symmetric plasmonic system. The results indicate optical angular momentum transfer to free electrons via a spin-momentum locking mechanism availed in surface plasmon polaritons.
Factors responsible and phases of speaker recognition system

The method of identifying a speaker based on his or her speech is known as automatic speaker recognition. Speaker/voice recognition is a biometric sensory device that recognizes people by their voices. Most speaker recognition systems nowadays are focused on spectral information, which means they use spectral information derived from speech signal segments of 10-30 ms in length. However, if the received speech signal contains some noise, the cepstral-based system&apos;s output suffers. The primary goal of the study is to see the various factors responsible for improved performance of the speaker recognition systems by modeling prosodic features, and phases of speaker recognition system. Furthermore, in the presence of background noise, the analysis focused on a text-independent speaker recognition system.
Cram on data recovery and backup cloud computing techniques

The present digital world technology is evolving at a rapid pace. To store, manage and protect the digital information, it is necessary to back up and recover the data with utmost efficiency. As a solution, cloud computing that offers customers a wide range of services can be used. Storage-as-a-Service (SaaS) is one of the cloud platform&apos;s services, in which a large volume of digital data is maintained in the cloud database. Enterprise&apos;s most sensitive data are stored in the cloud, ensuring that it is secure and accessible at all times and from all locations. At times, information may become unavailable due to natural disasters such as windstorms, rainfall, earthquakes, or any technical fault and accidental deletion. To ensure data security and availability under such circumstances, it is vital to have a good understanding of the data backup and recovery strategies. This chapter examines a variety of cloud computing backup and recovery techniques.
Research on the VR Technology in Basketball Training

With the rapid development of virtual reality technology in the field of life, sports simulation teaching has received unprecedented attention.VR technology provides teaching workers with advanced and scientific training methods, and basketball training simulation also promotes the development of computer technology.This paper mainly studies the feasibility analysis of VR technology in basketball training.Firstly, a human basketball training model is established, and then the images are preprocessed.Taking the denoised background grayscale image and the target frame grayscale image as the experimental objects, the difference operation is performed on the two images using the background difference principle introduced above.Finally, the bwlabel function is used to mark the coherent region in the differential binary image, and the area of the region can be calculated.The results show that VR technology makes the basketball training process more efficient, and players can interact with the virtual environment in a virtual environment to a natural way.
HOME: 3D Human–Object Mesh Topology-Enhanced Interaction Recognition in Images

Human–object interaction (HOI) recognition is a very challenging task due to the ambiguity brought by occlusions, viewpoints, and poses. Because of the limited interaction information in the image domain, extracting 3D features of a point cloud has been an important means to improve the recognition performance of HOI. However, the features neglect topological features of adjacent points at low level, and the deep topology relation between a human and an object at high level. In this paper, we present a 3D human–object mesh topology enhanced method (HOME) for HOI recognition in images. In the method, human–object mesh (HOM) is built by integrating the reconstructed human and object mesh from images firstly. Therefore, under the assumption that the interaction comes from the macroscopic pattern constructed by spatial position and microscopic topology of human–object, HOM is inputted into MeshCNN to extract the effective edge features by edge-based convolution from bottom to up, as the topological features that encode the invariance of the interaction relationship. At last, topological cues are fused with visual cues to enhance the recognition performance greatly. In the experiment, HOI recognition results have achieved an improvement of about 4.3% mean average precision (mAP) in the Rare cases of the HICO-DET dataset, which verifies the effectiveness of the proposed method.
Magnetization Vector 3D Inversions of Airborne Magnetic Data Flown Over Grib Kimberlite Pipe

Summary This study is describing mapping of Grib kimberlite pipe in Arhangelsk region, Russia using full magnetization vector parameters extracted from airborne magnetic data by means of 3D inversions. The inversion results were used to calculate corrected magnetic susceptibility, which is compared with the results of classical 3D magnetic susceptibility inversion. The corrected magnetic susceptibility allows to map Grib kimberlite pipe more efficiently. The full magnetization vector inversion was also supported by inversion of magnetic field on the basis of vector scanning with use of regularization via elastic net method for evaluation of Magnetic susceptibility and remanent magnetization. The final results are provided as case study and the described methodology can be further used for advanced kimberlite exploration.
Application of EMS training system in physical training

Based on the research that EMS training system simulates the brain signal of human body and directly transmits the motion signal to the muscle to make the muscle contract strongly, this study makes a horizontal analysis and comparison of normal athletes, rehabilitation people, obese people, whitecollar workers and other people, and makes a quantitative analysis.EMS training system is more efficient and effective than traditional training methods The advantage of convenience is to find a method to stimulate muscles through fast, effective and safe means, and find scientific and efficient training and rehabilitation methods for relevant people, which represents the future development direction of training and rehabilitation.
Application Risk Supervision Mechanism of Gene Editing Technology

The emergence and application of gene editing technology is one of the important symbols of human social progress, which also means that human beings can gradually treat diseases, eliminate poverty and prolong life by changing genes.However, the application of gold editing technology is in the exploratory stage.In this stage, if we can not establish a sound legislative model and relevant laws and regulations, it will inevitably lead to a series of risks.Therefore, from the perspective of the current application and development of gene editing technology in China, combined with China's basic national conditions, this paper discusses the application risk of gene editing technology in detail, and then puts forward its own views and suggestions on the construction of risk management mechanism of gene editing technology.
Computer Security Issues and Legal Systems Based on Cloud Computing

With the development of information technology in China, cloud computing technology has been universally used in the field of computer.The threshold of cloud computing gives rise to the possibility that the cost to utilize network information resources is reduced, endowing people with more extensive freedom of information, and thus civil and commercial activities are profoundly changed.The development of cloud computing has brought great impact on computer cyber security, and computer security has become a key issue required to be paid attention to in network development.We should draw lessons from foreign experience to formulate and perfect relevant laws and establish the mechanism of mutual cooperation and interaction between government functional departments.This paper summarizes the legal significance of cloud computing and puts forward the legal regulation strategy of computer security based on cloud computing.
Social Network Embedding Method Basing Loop-free Path

Network embedding's goal is to learn low-dimensional feature representations of nodes in a network and use the learned features in various analysis tasks of the network, such as node classification, link prediction, community discovery and recommendation.Existing network embedding methods do not make sufficient use of higher-order structural information in social networks and are not ideal for application in social networks.A Loop-free Path Combined with Attributes Network Embedding (LFNE) method is proposed to solve these problems.The algorithm first calculates the node higher-order structure similarity based on the loop-free paths between nodes, eliminates the influence of loop paths and large degree nodes on node structure similarity, and enables the network embedding method to better fuse the higher-order structure information of social networks.The node structural similarity is finally applied to the low-dimensional feature representation of the learned nodes in the stacked denoising autoencoder.Experimental comparisons with representative algorithms in recent years on three social network datasets show that the LFNE algorithm can achieve relatively significant results in node classification and link prediction experiments, with better network embedding performance.
Biased Edge Dropout in NIFTY for Fair Graph Representation Learning

Graph Neural Networks (GNNs) are nowadays widely used in many real-world applications.Nonetheless, the data relationships can be a source of biases based on sensitive attributes (e.g., gender or ethnicity).Several methods have been proposed to learn fair graph node representations.In this work we extend NIFTY, an approach that exploits additional terms in the loss function based on perturbing the input data to enforce the fairness of the GNNs.In particular, we exploit a biased perturbation of the adjacency matrix of the graph able to reduce the edge homophily.We show the effectiveness of our approach in four real-world graph datasets.
On Mathematical Models for the Discordant Size Content Replacement Attack in Watermarked Audio Signals

An important aspect of any watermarking scheme is its robustness against attacks. For this, it is essential to know the behavior of the attack when handling a signal. The discordant size content replacement attack is a very severe attack against audio watermarking schemes. This attack replaces a set of samples of an audio signal with another collection of samples of different sizes. This study presents two mathematical models that describe the discordant size content replacement attack using different approaches: matrix representation and decimation, and interpolation. Each model includes content replacement attacks of equal, larger, and smaller sizes. Evaluations are carried out with different degrees of manipulation. The objective is to obtain a mathematical description that analyzes the behavior of the attack on an audio signal, and use the models to design self-restorable schemes of audio signals that are robust against this attack. The models showed proper functioning in their evaluations.
Quality Assessment for Selected Primary Studies in a Systematic Literature Review: A Case Study

This paper describes the importance of evaluating the quality of the primary studies selected in a systematic literature review in the context of Software Engineering; the main criteria for quality evaluation are analyzed and the general procedure for this evaluation is presented. The evaluation is illustrated by a case study; additionally, the correlation between the variables selected in the study is explored.
Didactic Evaluation of Some Useful Predictive Methods in Neighborhood-Based Recommendation Systems

This paper presents a didactic-experimental study of three ratings prediction techniques that have been widely used in neighborhood-based Recommender Systems. The process of calculating the predictions and estimating the error for academic purposes is exemplified. The results of experimentation based on a MovieLens Dataset are presented. The objective was to measure the quality of the recommendations generated by the prediction techniques studied, with respect to the density of the data. As a measure of similarity, to generate the neighborhood, the Pearson correlation coefficient was used. To measure the quality of the recommendations, the metrics were used: mean absolute error and mean square error. The results show that the best predictive technique is what we have called F3, and that as the density of the data decreases, the error in the predictions increases.
Research on the Effective Ways of Education to Promote Rural Revitalization in Liaoning: Based on the Perspective of the Training Model of Outstanding Rural Teachers

The training of outstanding rural teachers has become an important strategic deployment of Chinese rural teacher support plan and teacher education reform."Outstanding rural teachers" means "training teachers with outstanding potential, teaching in rural areas and having rural education feelings".Grasp the key characteristics of outstanding rural teachers, explore theoretically constructing the outstanding rural teacher training system, expand and innovate training methods in practice, analyze the risks of outstanding rural teacher training and response plans in order to fully implement the training of outstanding rural teachers with opening new ideas and exploring new methods.
An Intelligent Recommendation Strategy for Online Courses Based on Collaborative Filtering Algorithm for Educational Platforms

In the context of the new crown epidemic, online education courses have become mainstream.Along with the rapid development of the Internet and the rich reserves of educational resources, how to accurately understand the state of students to provide the required teaching courses, and how to better adapt students to online education to meet the differentiation between each other has become a mainstream problem and challenge at present.Analyze the user data, obtain the decision attributes therein, and explore the classification of online education courses; based on collaborative filtering algorithm, realize the accurate matching between students and courses.The intelligent recommendation strategy of online courses not only helps to improve the usage rate of online courses, but also helps to improve students' individuality, responding to the general policy of "stopping classes and not studying" in major universities, and carrying out more flexible teaching methods.
The Role of the Ruling Dimensions of Artificial Intelligence in Increasing the Efficiency of e-Learning Entrepreneurship

Purpose: The main purpose of the research is to achieve E-learning in university education through the strategic role of Artificial Intelligence (AI) in order to bring about development of university educational systems and continue to maximize competitiveness at the local, regional and global levels. Practical implications: Universities and specialized ministries seek to adopt leadership in e-learning to achieve the ability to in-depth thinking and data analysis in the light of the Sustainable Development Strategy. Originality/Value: It is necessary to move towards adopting artificial intelligence applications in university education to greatly enhance human capabilities and contributions. E-learning. A research article covering the framework of achieving university E-learning. Results: The dimensions of artificial intelligence have a strategic and pivotal role in building Technological Educational Programs and curricula that achieve the highest performance rates for human resources
Managing Large Server Environments

You have made it to the last chapter of this book. So far, this book has focused a lot on practical real-world examples to help you learn how to use Ansible to manage and configure your technology infrastructure. Although we have done our best to give you real-world examples on how to manage these environments, there is one thing that might be different when you try to implement Ansible into your organization, and this is possibly the number of the hosts and servers you are working with.
ReBLS Denoiser: An Effective Denoiser based on the Residual Broad Learning System for CT Images

Image denoising plays an important role in CT sampling. Although good results have been achieved by filter-based, model-based and deep learning-based denoising methods, there are still many demanding constraints in real applications. In order to improve accuracy and efficiency, a residual learning network based on broad learning system (ReBLS) is proposed. First, features are extracted from the noisy images to generate a feature node layer. Subsequently, through a random matrix after orthogonal normalization, the feature nodes are reinforced into an enhanced node layer. Finally, the noise will be removed by the residual network. Compared with commonly used deep learning scheme, the broad learning replaces depth with expansion in breadth, the training and process time of broad learning is much shorter. Meanwhile, the learning of residuals can simplify the learning process and enhance the feature extraction. Experiments on the open dataset fully prove the accuracy and efficiency by comparing with five state-of-the-art denoising methods.
PENYU DALAM KARYA SENI GRAFIS INTAGLIO PRINT

Sea turtles (Chelonioidea) can be found in all oceans except for the polar regions. Sea turtles have a pair of front limbs as rower legs which make swimming easier. Althought it stays in the water for the rest of its life, it occasionally has to go on surface of the water to take a breath.Sea turtles, in this artwork aims to visualizes an expression of the writer's anxiety about the endangered turtle population. This artwork made by the intaglio print technique as one of the graphic art techniques. After various observations and long steps of work, these 10 artworks created and displayed namely : 1) Pemangsa, 2) Pencurian telur, 3) Ancaman, 4) Abnormal, 5) Harapan, 6) Terdampar, 7) Memakan plastik, 8) Korban, 9) Impian, 10) Terjerat. This is the result of sea turtles artwork which illustrates the writer's anxiety about the current phenomenon. Besides, this graphic work is expected to be a source of encouragement for other creators.Keywords: Sea Turtles, Graphic Art, Intaglio Print