Reviewerly/exHarmony huggingface dataset:
Neuron perceptron-driven image encryption incorporating hyper-chaotic system

An algorithm of image encryption based on hyper-chaotic system and neuron mechanism as well as encryption sequence encoding of image high and low bit plane diffusion and zigzag scrambling fusion is proposed. First, the plaintext image is divided into eight-bit planes, and then the high and low four-bit planes are randomly exchanged and merged to obtain a diffusion image. The diffusion image is encrypted and encoded using the neuron mechanism, and then the Zigzag scrambling operation is performed. Perform further forward and backward diffusion operations on the scrambled image to get the ciphertext image. The analysis of the experimental results can prove that the encryption algorithm of this paper has a large key space, strong anti-attack ability, and excellent cryptographic performance.
Research on General Text Classification Model Integrating Character-Level Attention and Multi-Scale Features

To solve the problem of poor interpretability of the model caused by the random initialization of convolution kernel in convolution neural network,and the problem of local and single feature extraction scale, a general character-level classification model is designed by referring to the method in CV. Firstly,a multi-scale feature extraction module is added to the network embedding layer to extract rich context information, and the problem of matrix sparsity is solved to some extent by setting different void rates. Then, the network depth is controlled by the number of blocks. Different depths have different grasps of global information and can adapt to tasks with different complexity. Next, add a modified attention mechanism module after the block to enhance the attention of the model to key parts. Finally, the full connection layer of the network is replaced by the full convolution layer to simplify the model. The block is a compressed version of deep separable convolution, and the overall model parameters are reduced to about one-tenth of the original, but the accuracy and performance are improved. The results show that the model is very effective.
«ZEMSTVO TEACHER» PROGRAM: PAST AND PRESENT

The article discusses the tasks and prospects of implementing the state program «Zemstvo teacher», provides the main criteria for competitive selection of candidates, provides statistics on the implementation of the program in certain regions of the country, demonstrating the interest of teachers in the program. The comparative historical and pedagogical analysis of certain provisions of the program «zemstvo teacher» with experience of Zemstvo institutions in the XIX-XX centuries. Special attention is paid to the personality of the Zemstvo teacher, his moral qualities, selflessness, dedication to his work, evidence, historical facts of difficult working conditions and life of teachers are provided. It is noted that despite the low level of equipment in rural schools, lack of textbooks, lack of premises for classes, education was developed in the Russian hinterland, the number of schools and students was increased, educational training was transformed, and there was training in crafts. Examples are given of the fact that the activities of Zemstvo teachers were not limited to teaching subjects in the classroom, but extended to educating the population as a whole, setting up reading rooms, lodging houses, shelters for the destitute, assistance and assistance to the poor. Historical Parallels are illustrated by local examples of school work in the modern Republic of Karelia and the former Olonets province, as proof, excerpts from documentary sources are given, and the views of different authors are compared. In the final part, the author presents the opinions of modern teacherscientists on the need to take into account the historical experience of rural teachers of the past, draws conclusions about the features of the situation of modern rural teachers and their role in the development of rural society. The author emphasizes the need for comprehensive support of a modern zemstvo teacher, the creation of meaningful, sociopsychological, organizational-pedagogical and socio-economic conditions, the organization of support and support services, and the experience of organizing pedagogical communities on the example of the Yaroslavl public organization «Leaders of rural schools».
Introduction

This chapter is devoted to the basics of quantum information processing (QIP), quantum computing, and quantum error correction (QEC) as well as to the organization of the book. The chapter starts with the photon polarization description because it represents a simple and the most natural connection to the QIP. In the same section, we introduce the basic concepts of quantum mechanics, including the concept of the quantum state. We also introduce the Dirac notation, which will be later used throughout the book. We then formally introduce the concept of qubit and provide the corresponding geometric interpretation. We then provide another relevant realization of qubits, the spin-1/2 system. The focus then moves to the basic quantum gates and QIP fundamentals, followed by the quantum teleportation basics. Further, the basic QEC concepts are introduced. The basic concepts from quantum key distribution (QKD), also known as quantum cryptography, are introduced next. The detailed organization of the book is provided at the end of the chapter.
Integrating Multimodal Courses into Mobile Learning in International Chinese Education

Mobile learning provides a new teaching platform for international Chinese language teaching. To adapt to this new way, a variety of teaching methods is required for diversified teaching design. Multimodal courses can be integrated into this demand, which helps to create a multi-sensory teaching and learning atmosphere to enhance students’ learning initiatives. It meets the designing requirements of mobile learning and can optimize learning quality. In this study, we selected the listening and speaking class “What’s the date today?” (Jīntiān jǐ yuè jǐ hào?) that won the gold medal in an international Chinese teaching competition, annotated its modalities, and explored the class’s features in different teaching stages. It provides a new perspective for the curriculum design for mobile learning. This paper also analyzed the shortcomings of traditional listening and speaking courses, summarized the features of a multimodal course, and suggested how to integrate multimodal courses into mobile learning in order to enhancing the student’s learning experience.
Daphne Oram

In her book An Individual Note (1972), Daphne Oram developed multiple extended analogies between humans and electronic sound technologies. Oram used these to suggest how “music and information theory, allied to technology” might be applied to mental health; and to support her design for a machine that would preserve human qualities through its interactivity and musical results. This music machine, Oram wrote, “is based mainly on controlled feedback and the computing of resultants.…It is a control system which could be applied to many fields, as well as music.” Analogies between organisms and machines, core concepts of control and feedback, information theory: These suggest a cybernetic cast to Oram’s work. As Dunbar-Hester (2010) has observed, however, some experimental musicians drew directly from cybernetic sources, while others employed cybernetic-sounding language without such explicit engagement. To date, Oram has figured in neither camp, scholarly interests having been directed primarily toward her pioneering position in the male-dominated field of electronic music. Drawing on archival work at the Daphne Oram Collection at Goldsmiths, University of London, this article uncovers sources and conditions that informed Oram’s relationship to cybernetic science and aesthetics. As Oram pursued a program of music research and invention in the 1960s–70s, cybernetics offered a vocabulary, methodology, and ontology that interfaced the worlds of computer art and scientific progress with those of spiritualism and higher sense perception. At the same time, Oram’s commitment to authorial control for the composer put her at odds with the interests in indeterminacy and self-generating systems predominant among artists publicly recognized as cybernetic. The uneasy fit between Oram’s work and music’s cybernetic canon points toward a “scientific-spiritual space” (Pickering, 2010) that existing historiographies of electronic music have not fully recognized, and shows there is far more than Oram as woman that needs to be written back into history.
Selection and Prediction of the Trend of a Time Series Using a Recurrent Neural Network

For preprocessed time realizations, envelopes are constructed using interpolation methods and the signal trend is highlighted. The resulting trend is divided into two samples, according to which the recurrent neural network is trained. Various variants of the neural network architecture, including those using the LSTM module, are considered. The Adam optimization algorithm is used to train the model. The implementation is made in Python using the PyTorch framework. The dependence of the forecast quality on the size of the input data sequence is considered.
Design of a Social Network Data Deep Learning Recommendation System Guided by User Preference

The currently designed social network data deep learning recommendation system has a low matching degree of recommendation information and poor user satisfaction. To solve the above problems, a new social network data deep learning recommendation system is designed under the guidance of user preferences, and the hardware and software of the system are optimized. The system hardware is mainly composed of an information collector, information storage analyzer, recommender, data communicator, clock chip, sensor and memory. Through data analysis, measuring diversity and matching, and calculating perceived time, we complete the recommendation of in-depth learning. The experimental results show that the social network data deep learning recommendation system designed under the guidance of user preference has good information matching ability, can improve user satisfaction and reduce recommendation error.
A Quantum-State-Based Change Formalism for Radar Sensing and Potential Applications for Dual and Compact Polarimetric SAR

A quantum-state-based technique for change detection and analysis is presented. The method establishes a framework for change detection based on quantum states’ entanglement concept. The approach is applicable to quantum sensing (QS) (i.e., low field intensity, photon pairs) but is contingent to feasibility of quantum implementations and measurements. The main objective in this work is to develop a new change detection technique for classical radar/synthetic aperture radar (SAR) remote sensing that is inspired by quantum illumination (QI) and exploits the underlying characteristics and mathematical analogy between quantum-states and radar scatter-field-states. The described application of QI concept to develop a quantum signal processing (QSP) algorithm for classical radar/SAR data exploitations introduces a new change detection approach for SAR multi-pol exploitations.
Application of Artificial Intelligence in Water Conservancy Project Management

With the continuous improvement of the country’s economic level, all aspects of the country’s construction have achieved world-renowned achievements. Especially in the field of science and technology, major breakthroughs have been made, which are mainly manifested in the use of Internet technology, big data technology and artificial intelligence technology, etc., which have played an important role in the daily life of the people. Artificial intelligence technology is currently the main technological commanding height of competition among countries in the world. This article takes the application of artificial intelligence in water conservancy project management as the topic, and through detailed introduction of its concept, meaning and related methods of application, it will further enhance people’s understanding of artificial intelligence technology to achieve the purpose of providing a reference for the people.
Safeguard the Original Data in Federated Learning via Data Decomposition

In federated learning, more and more studies have discovered that attackers can recover the original data from the shared gradients of participants. However, existing defense models struggle to balance the privacy of participants and the effectiveness of federated learning in the face of cutting-edge attack models. Therefore, we propose a powerful defense model to protect the original data while ensuring the effect of classification. First, we get two featured datasets from original data based on Sparse Dictionary Learning (DL) or QR decomposition. In these two featured datasets, we select one dataset to replace the original data for federated training named co-trained data, and the other one is kept on the local client named left data. At this point, the adversary in federated learning can only obtain co-trained data, which cannot recover the original data due to the lack of left data. Following the completion of the federated learning, the participant requests a parameter from the server. Using this parameter, we can combine the aggregated global model over co-trained data with the offline-trained local model of an arbitrary participant to develop the final classification results. Some theories and a lot of experiments demonstrate the classification effectiveness of our model. It also can be a general solution to the original data leakage problems caused by gradient leakage.
Analytical Approach for Sentiment Analysis of Movie Reviews Using CNN and LSTM

With the rapid growth of technology and easier access to the internet, several forums like Twitter, Facebook, Instagram, etc., have come up, providing people with a space to express their opinions and reviews about anything and everything happening in the world. Movies are widely appreciated and criticized art forms. They are a significant source of entertainment and lead to web forums like IMDB and amazon reviews for users to give their feedback about the movies and web series. These reviews and feedback draw incredible consideration from scientists and researchers to capture the vital information from the data. Although this information is unstructured, it is very crucial. Deep learning and machine learning have grown as powerful tools examining the polarity of the sentiments communicated in the review, known as ‘opinion mining’ or ‘sentiment classification.’ Sentiment analysis has become the most dynamic exploration in NLP (natural language processing) as text frequently conveys rich semantics helpful for analyzing. With ongoing advancement in deep learning, the capacity to analyze this content has enhanced significantly. Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) is primarily implemented as powerful deep learning techniques in Natural Language Processing tasks. This study covers an exhaustive study of sentiment analysis of movie reviews using CNN and LSTM by elaborating the approaches, datasets, results, and limitations.
A Fitness Education and Scoring System Based on 3D Human Body Reconstruction

Affected by the Covid-19 epidemic, online fitness education has attracted a large number of users. However, when there are a large number of students in a same online classroom, it is difficult to get the coach’s advice and scores in time. To overcome this problem, we propose an AI fitness education system that uses 3D reconstruction technology to restore the shape of the human body and its bones. The skeleton is used for posture scoring. The 3D human body model is reconstructed by our improved VIBE network, with the accurate posture, shape and movement of the coach and students. By adding the loss function of the end limbs to the 3D human body model, compared with the performance of the original VIBE, we reduce the jitter noise in continuous motion. The training results show the accuracy of our improved VIBE. In addition, we have also established a scoring system, which can score the posture of the trainees based on the coach’s posture, and provide feedback through visual tag points. The experimental results show that our method is feasible and worthy of further exploration.
The Dynamics of Cryptocurrency Liquidity Pools

We assess the dynamics of the cryptocurrency liquidity pools at the heart of Decentralised Finance (DeFi). We distinguish between liquidity provision and liquidity demand, and show that yields, rather than risks, are the primary factor in determining liquidity provision to these pools. While, for liquidity demand there is an additional importance of relative price fluctuation as a risk factor. We further show that price shocks in leading cryptocurrencies cause persistent shocks across liquidity pools. The initial findings from this first exploration of decentralized cryptocurrency liquidity pool dynamics are suggestive of reasonably well-functioning markets.
Integrating Fuzzy Logic and Dynamic Programming in Multithreaded Concurrent File Transfer Schemes

The advancement in the architecture of multicore processors leads to the rapid development of multithreaded concurrent file transfer utilities. The ability to split a process into subprocesses and perform multiple series of Input/Output (I/O) read-write file operations simultaneously aids the program to speed up the data transmission process. However, copying or moving files using multithreading techniques has a shortcoming when with dealing I/O file operations that are performed within multiple threads especially when there is a bulk of background processes taking place in the system or the system hardware profile is low. In this paper, we propose an alternative scheme for multithreaded concurrent file transfer methods by integrating it with fuzzy logic and dynamic programming (DP) algorithm. Using the proposed model will maintain a good balance on the number of threads being spawned and distributes a consistent number and weight of the files to be processed for each thread, thus, making it more smart and effective in handling shared resources and improving the data transmission rate.
Younger face recognition by learning perceptual flows

In spite of the extensive research that has been performed on age detection with a single face image, little attention has been paid to younger face recognition given an image pair. Our paper addresses the problem by proposing a perceptual flow extraction algorithm, followed by a CNN based transfer learning for the recognition purpose. In particular, our algorithm first employs just noticeable difference scheme for filtering face images, so that the noise that cannot be perceived by the human visual system is reduced. Then, by fusing Laplacian stacks into our flow approximation framework, the estimated perceptual flow is able to capture features at multiple scales. Due to the lack of data and the difficulty of collecting and annotating face image pairs, we have also introduced a dataset based on the UTKFace dataset for the purpose. Experiments demonstrate the effectiveness of our approach.
Inflated 3D Architecture for South Indian Sign Language Recognition

The inability to speak is considered to be a true disability that affects both the speaker and the listener. Therefore, there is a need for a method that can recognize sign languages so that a successful communication is established. This paper proposes a method that classifies South-Indian Sign Language and translates it in real-time to Kannada language. The first few samples for the dataset were obtained from the official ISL website while the rest of the samples were manually recorded. Optical flow method is employed to extract the motion features in a video and the resulting frames are used as input to the proposed Inflated-3D model which recognizes the sign language. Stratified k-fold cross- validation is used to improve the performance. The predicted sign in text form is then translated to Kannada language through the use of GoogleTrans API and further synthesized into a speech segment using the GTTS open-source library. In addition, a comparative study of the proposed methodology with other techniques that have been proposed to recognize sign languages through video streams has been presented. The proposed method resulted in an average accuracy of 0.8709.
SSGTA: A Novel Swap Sequence based Game Theory Algorithm for Traveling Salesman Problem

Many approaches have been developed to make intelligent moves imitating rational decision-makers. Game theory provides a theoretical framework that can be efficiently employed in solving complex optimization problems. The area of applied mathematics that investigates the strategic behavior of rational factors is known as game theory. In other terms, game theory is an analytical tool for making the optimal decision in interaction and decision-making situations. The Traveling Salesman Problem (TSP) is solved using this research’s swap sequence-based game theory algorithm (SSGTA). TSP is a well-known combinatorial optimization real problem. TSP is also widely used to assess expertise in newly emerging optimization techniques. Furthermore, optimization techniques established for other tasks (such as numerical optimization) are tested for TSP competency. A player attempts to update its solution using another player. An expected payoff mechanism is proposed to choose the learning strategy. Based on the improvement of solution quality, a payoff is awarded to the winning player. When no improvement is noticed in the solution, the 2-opt algorithm is employed to get over the local optimal. SSGTA is tested for several benchmark TSP instances from TSPLIB and compared with some other recent methods. SSGTA performs better than different algorithms on accuracy and stability.
A Corpus-Based Sampling to Build Training Data Set for Extracting Japanese Sentence Pattern

Training data set plays an important role in Natural Language Processing (NLP) or Machine Learning (ML) Tasks. In the application of NLP in Japanese language education, construction of a high-quality training data set becomes the pre-requisite of automatic extraction of grammar knowledge where there are limited training data sets are available. In this work, a corpus-based method for building training data set was proposed aiming to reach a satisfactory performance in automatic extraction of Japanese sentence patterns in Japanese grammar. Furthermore, a machine learning algorithm based on Conditional Random Field (CRF) was applied to train a model using the manually annotated training data sets in experiments. A comparative evaluation was conducted in terms of our proposed method and a baseline method based on paper-based sampling. Experimental results indicated that our proposed method based on corpus-based sampling to build training data set achieved much higher accuracy than paper-based sampling.