Reviewerly/exHarmony huggingface dataset:
Aesthetic Index Analysis of Fusion of Folk Martial Arts and Dance Based on Deep Learning Algorithm

In this paper, we propose a computable method to evaluate the aesthetic value of fusion images of folk martial arts and dance based on human visual and aesthetic habits, extract features and use them as evaluation indexes from three aspects: technical features, perceptual features, and social features, establish an aesthetic evaluation model by fusing each index, and determine the influence factors of each index by using online research. The aesthetic evaluation of fusion images of folk martial arts and dance is carried out automatically. The results of the experimental tests on the evaluation dataset of the aesthetic quality of fusion images of folk martial arts and dance at the Communication University of China show that the accuracy of the proposed deep learning algorithm-based method for analyzing the aesthetic index of fusion of folk martial arts and dance can reach 98.08% with certain validity, and the evaluation results of each index are clear and intuitive, which can play a guiding role in improving the aesthetic quality of fusion of folk martial arts and dance from various angles. It can be used as a guide to improving the aesthetics of the fusion of folk martial arts and dance from various perspectives. By integrating the model into the mobile application, it is possible to evaluate and score the aesthetics of multiple portrait photos uploaded by users and select photos to be kept or deleted based on the scoring results, thus simplifying user operations.
Search Engines: From the Lab to the Engine Room, and Back

Prabhakar Raghavan has given a Keynote Talk at The ACM Web Conference 2022 on Wednesday 27th April 2022. This paper provides a summary of the topics he addressed during his talk.
Passive Light Source Monitoring for Sending or Not Sending Twin-Field Quantum Key Distribution

Twin-field quantum key distribution (TF-QKD) can break the repeaterless linear bound and possess the measurement-device-independent security, and thus seems very promising in practical applications of quantum secure communication. In most reported TF-QKD protocols, light sources are assumed to possess trusted and fixed photon number distributions (PND), which are unrealistic assumptions in practical applications. Fortunately, the light source monitoring (LSM) method is proposed to circumvent this problem by actively adjusting the attenuation coefficient and monitoring the photon number distribution of light sources. However, the active light source monitoring (ALSM) method may induce additional modulation errors due to imperfect attenuation devices, deteriorating practical performances of TF-QKD systems. In this manuscript, we propose a passive light source monitoring (PLSM) scheme for TF-QKD, and employ the sending-or-not-sending (SNS) TF-QKD as an example for illustration. Simulation results show that our present work can greatly exceed both the original SNS protocol and the ALSM scheme when light source fluctuations and modulation errors are taken into account.
Effectiveness Evaluation Model of Students’ English Listening Ability Based on Immersive Computing

In the context of the integration of China’s international economic development, the country has an increasing demand for high-level English professionals. Therefore, schools need to strengthen the teaching of students’ English learning and improve students’ comprehensive English ability. College English listening course is an important course to cultivate students’ English skills. Improving the teaching effect of college English listening course has become an urgent problem in English teaching. With the development of China’s network technology and education reform, a brand-new three-dimensional virtual immersive environment teaching model has emerged. This teaching model is of great help to students in learning English. This paper applies immersion theory to college English listening teaching, using virtual reality technology to create a three-dimensional virtual immersive environment for English listening teaching and using this new environment to promote English listening teaching, stimulate students’ interest in English learning, and improve students’ English. The ability of listening comprehensively improves the efficiency of English listening teaching, thereby making college English listening lessons lively and interesting and rich in information, and helps students have an immersive experience, so as to cultivate students’ interest in learning, form learning motivation, and ultimately achieve the goal of improving their English proficiency. The method proposed in this article organically integrates the teaching, learning, and evaluation of English listening. First of all, it can evaluate the attribute grasping mode and its advantages and disadvantages of groups and individuals in the process of listening and cognition. Relying on the network, it can also provide real-time networked diagnosis reports and targeted guidance. Secondly, teachers can discover the characteristics and needs of individuals and groups based on the diagnosis results, adjust and optimize their own teaching methods, and truly teach students in accordance with their aptitude. More importantly, students can find problems through self-diagnosis and obtain effective guidance and intervention, which finally help them to move towards autonomous learning. This is also the ultimate goal of modern English education. From this perspective, this research also helps to open up new directions for future English research.
Data Scholarship and Student Engagement

The purpose of this chapter is to introduce library-led data scholarship and student engagement, demonstrate the extra-curricular achievements of students, and evaluate the pedagogical framework for student investigations. The objective of this chapter is to examine key aspects of the data scholarship ecosystem, including building data scholarship as a partnership between librarians, students, and academics; developing the knowledge, skills, and experiences of both librarians and students in data scholarship; and delivering a case study from specific Chinese contexts in the shaping of the extra-curriculum for student success, which includes student works and the student voice.
Neutro Algebra and Neutro Group

This chapter improves and extends several definitions, properties, and applications of neutro-algebras. In classical algebraic structures, the laws of compositions on a given set are well-defined. In fuzzy algebraic structures, the laws of compositions on a given set are not well-defined exactly but with a membership value. This chapter examines those structures which have huge applications or strong potential for applications outside of mathematics, namely computer science or engineering. The study emphasizes that the nature of neutro-algebra and anti-neutro algebra differs from that of the classical algebra and fuzzy algebra. This chapter presents Venn diagram for neutro-function and establishes some relations between the partial algebra and neutro-algebra. The main contribution of the chapter is the introduction of the notion of neutro-group.
Context Aware Attention Learning For Person Re-Identification (A Data Driven Approach)

Deep learning approaches have started to conquer the research progress of video-based person re-identification (re-id). However, comprehensive manual efforts are required for existing approaches which is substantially consider supervised learning. The proposed context aware learning system that considers human body features and also considers the feature points extracted from the surrounding. It extracts the feature points and matches the person from videos captured across network of surveillance cameras. For example, People are identified with biometrics like face, iris, or gait. A high level of segmentation (such as at airports and subway stations) and/or poor camera quality make it impossible to get specific information in most video surveillance setups. For re-identification, a strong modeling of an individual’s overall look (apparel) is necessary. This is a particularly difficult challenge because of the large appearance changes produced by differences in view angle, lighting circumstances, and the individual’s varied postures. This approach is very much robust in pedestrian videos.
Fast classification and recognition method of blood cells using deep learning based on wrapped phase in polar coordinate

In order to realize the nondestructive, fast and accurate classification of batch of blood cells, a classification method using deep learning based on wrapped phases in polar coordinate is proposed. In this method, after the coordinate transformation, the wrapped phases of cells are obtained first, and then three characteristic parameters of symmetry, roundness and singularity are established to analyze the phases. Subsequently, the data set of wrapped phases is built with the data augmentation. Finally, the classification and identification of batch of blood cells are realized through the convolutional neural network training based on LeNet-5 and the stochastic gradient descent algorithm (SGDM). An excellent result of the classification accuracy rate of 100% is obtained, which proves that the method has good feasibility and high accuracy. This method only requires the wrapped phases of cells in polar coordinates, so the complex unwrapping procedure and the additional noise effects are avoided.
Secure ‘Text in Image’ Steganography Scheme Based on Alexnet and Contour-Let Transform

Nowadays, ‘text in image’ steganography is utilised in a variety of applications like military, surveillance, and remote sensing etc., in order to keep the secret information secure. This paper is presenting the unique steganography algorithm for improving data security, visual quality, quality metrics and withstand the attacks of the stego image. In proposed algorithm, the security of the confidential information is improved by utilizing the SLICE encryption algorithm. It is used to generate the cipher data from the confidential data. The alexnet is introduced in this paper to detect the facial area in human cover image. A total of 1987 images are used to train the network and got maximum accuracy. Contour-let transform is utilized to decomposition of alexnet output. The “random pixel embedding” (RPE) technique is utilized to embed the confidential data in facial area of the sub-band. The combination of alexnet and contourlet transform is used to generate good visual quality of the stego image. The proposed algorithm produces a stego image that has better visual quality, security, metric-values and withstand attacks compared to recent methods.
Aspect Based Sentiment Analysis for Customer Reviews

In todays world people buy their product based on customer This project aims at providing a service for business which would do aspect-based product rating and data visualizations using the existing reviews of the product. In online shopping sites or in movie-info sites, many user reviews are present. Many of these reviews contain useful specific information about the various features of the product. In the existing systems, we do not get accurate sentiment results if we review sentences having more than one aspect about the product or service. Various aspects may be present in the sentence with varying polarities and this may lead to the misinterpretation of the products or services to the customers online. Performing natural language processing using the Natural Language Tool Kit (NLTK) on these structured data and doing sentiment analysis on the processed text, will help us to get aspect-based sentiment analysis of a particular feature whether it is good or bad. These analyses are later processed by a mathematical model and converted into individual features rating. We get the results by providing the aspects present in the customer review and display its rating out of 5 using a mathematical model. It helps to provide an easy way for customers to know about the goodness of a feature in a product through real-world customer reviews rather than the specification of the feature.
Few-Shot Object Detection with Proposal Balance Refinement

Few-shot object detection has gained significant attention in recent years as it has the potential to greatly reduce the reliance on large amounts of manually annotated bounding boxes. While most existing few-shot object detection literature primarily focuses on bounding box classification by obtaining as discriminative feature embeddings as possible, we emphasize the necessity of handling the lack of intersection-over-union (IoU) variations induced by a biased distribution of novel samples. In this paper, we analyze the IoU imbalance that is caused by the relatively high number of low-quality region proposals, and reveal that it plays a critical role in improving few-shot learning capabilities. The well-known two stage fine-tuning technique causes insufficient quality and quantity of the novel positive samples, which hinders the effective object detection of unseen novel classes. To alleviate this issue, we present a few-shot object detection model with proposal balance refinement, a simple yet effective approach in learning object proposals using an auxiliary sequential bounding box refinement process. This process enables the detector to be optimized on the various IoU scores through additional novel class samples. To fully exploit our sequential stage architecture, we revise the fine-tuning strategy and expose the Region Proposal Network to the novel classes in order to provide increased learning opportunities for the region-of-interest (RoI) classifiers and regressors. Our extensive assessments on PASCAL VOC and COCO demonstrate that our framework substantially outperforms other existing few-shot object detection approaches.
FEATURES OF PROFESSIONAL SELF-DETERMINATION OF FUTURE TEACHERS BY THE CREATIVE MEDIA USED

The scientific problems of self-determination as well as the practical content of the implementation of the professional development of the future specialist through the creatively used media are considered and studied. In addition, it is written about the importance of various directions and ways of implementing the professional selfdetermination of a future specialist and ways of forming factors of professional self-determination. The need for value self-determination, formed in the student, and the role of pedagogical, psychological literacy in the orientation towards professionalism are investigated. Thus, the question of the formation of the perception and implementation of an arbitrary personality structure in solving theoretical and practical problems is considered. The versatility and versatility of the content and ways of realizing the professional self-determination of future specialists through the media are described.
A Primary Study: Automatic Ultrasound Acquisition System Based on Visual Servo

For the automatic ultrasound (US) acquisition system, the current work mainly uses the image-based visual servo(IBVS) strategy, making the specially designed visual features unable to adapt to all organs. This work proposes a position-based visual servo (PBVS) strategy to control the US probe to collect high-quality US images. Firstly, we use the force control strategy to make up for the positioning error of the initial acoustic window to ensure good contact between the US probe and the patient and the patient's safety. Then, This work set a spiral path for each region of interest to traverse the region of interest. In addition, because the spiral path is a fixed path planned after the initial acoustic window is calculated, the occlusion problem can be effectively solved.
End-to-end training of convolutional neural network for 3D hand pose estimation in dual-view RGB image

Gesture based robot human-computer interaction is more natural and convenient. Accurate hand pose estimation has always been a research hotspot in the field of human-computer interaction. This paper proposes an end-to-end training hand pose estimation method based on convolutional neural network for ordinary RGB gesture images. This method can establish a data set containing pose data for the hand to be estimated, and use convolutional neural network to fit gesture images and pose angle data. In addition, aiming at the problem that gesture self-occlusion affects the accuracy of hand pose estimation, this paper proposes a strategy of hand pose estimation model training and prediction using dual-view images. Through experimental verification and analysis, the hand pose estimation method has achieved good test results on the unknown test sample set. The method has the advantages of simple design, convenient use and high accuracy.
Provable data possession based Multi-Cloud Storage Security with Auditing

The data are stored as multiple copies on different cloud servers for improving the constancy and availability as the data are being outsourced. For proving the integrity of the data of multiple copies Provable Data Possession (PDP) protocol is used. Beforehand all of the PDP protocol will be storing copies in single cloud storage server. Public key infrastructure was depended by many PDP protocols which lacks security and leads to vulnerabilities. For storing various copies on multiple different cloud server identity based provable data possession has been used. By using the homomorphic tags data are stored in multiple cloud and its integrity will be simultaneously checked. Computation Diffie-Hellman hard problem was base for our scheme. Our scheme has been the premier for the provable data possession of multifold copies on multiple various cloud. The given system model, security model was given and this experimental research proved that our PDP scheme is applicable as well as practical.
Online learning agents for cost-sensitive topical data acquisition from the web

Access to one of the richest data sources in the world, the web, is not possible without cost. Often, this cost is not taken into account in data acquisition processes. In this paper, we introduce the Learning Agents (LA) method for automatic topical data acquisition from the web with minimum bandwidth usage and the lowest cost. The proposed LA method uses online learning topical crawlers. The online learning capability makes the LA able to dynamically adapt to the properties of web pages during the crawling process of the target topic, and learn an effective combination of a set of link scoring criteria for that topic. That way, the LA resolves the challenge in the mechanism of combining the outputs of different criteria for computing the value of following a link, in the formerly approaches, and increases the efficiency of the crawlers. A version of the LA method is implemented that uses a collection of topical content analyzers for scoring the links. The learning ability in the implemented LA resolves the challenge of the unclear appropriate size of link contexts for pages of different topics. Using standard metrics in empirical evaluation indicates that when non-learning methods show inefficiency, the learning capability of LA significantly increases the efficiency of topical crawling, and achieves the state of the art results.
A Deep Neural Network Based on Circular Representation for Target Detection

Convolutional neural network (CNN) model based on deep learning has excellent performance for target detection. However, the detection effect is poor when the object is circular or tubular because most of the existing object detection methods are based on the traditional rectangular box to detect and recognize objects. To solve the problem, we propose the circular representation structure and RepVGG module on the basis of CenterNet and expand the network prediction structure, thus proposing a high-precision and high-efficiency lightweight circular object detection method RebarDet. Specifically, circular tubular type objects will be optimized by replacing the traditional rectangular box with a circular box. Second, we improve the resolution of the network feature map and the upper limit of the number of objects detected in a single detect to achieve the expansion of the network prediction structure, optimized for the dense phenomenon that often occurs in circular tubular objects. Finally, the multibranch topology of RepVGG is introduced to sum the feature information extracted by different convolution modules, which improves the ability of the convolution module to extract information. We conducted extensive experiments on rebar datasets and used AB-Score as a new evaluation method to evaluate RebarDet. The experimental results show that RebarDet can achieve a detection accuracy of up to 0.8114 and a model inference speed of 6.9 fps while maintaining a moderate amount of parameters, which is superior to other mainstream object detection models and verifies the effectiveness of our proposed method. At the same time, RebarDet’s high precision detection of round tubular objects facilitates enterprise intelligent manufacturing processes.
On NeutroAlgebra Environment for Image Retrieval

A NeutroStructure is a structure that has at least one NeutroRelation or one NeutroAttribute, and neither AntiRelation nor AntiAttribute. In this chapter, the authors review and study the NeutroAlgebra structure and introduce several operations and properties of algebraic equations for image retrieval, such as NeutroContent-based image retrieval. It is a generalization of classical and fuzzy algebraic structures for image retrieval. An explanation of the new particular processes is provided by several results.
Emotions on Edge - the Dependence of Different Characteristics of the Convolutional Neural Network on the Number of Classes

Machine learning is most often associated with powerful computers, but lately, it is increasingly present on both microcontrollers and systems on a chip. Initially, simple machine learning algorithms were used on these platforms, but today we already see high-resolution cameras and convolutional neural networks used to process camera data. Models for such systems are prepared on large and powerful computers, but previously trained neural networks could be copied and used on microcontrollers and systems on a chip. This paper aims to measure the performance and characteristics of systems on a chip in which a convolution neural network for recognizing facial emotions is implemented. A different number of emotions are used to determine the impact of this change on performance, and the effect of quantization on performance and model size is also analyzed.
The Use of Reactive Programming in the Proposed Model for Cloud Security Controlled by ITSS

Reactive programming is a popular paradigm that has been used as a new solution in our proposed model for security in the cloud. In this context, we have been able to reduce the execution time compared to our previous work for the model proposed in cloud security, where the control of security depending on the ITSS (IT security specialist) of a certain organization based on selecting options. Some of the difficulties we encountered in our previous work while using traditional programming were the coordination of parallel processes and the modification of real-time data. This study provides results for two methods of programming based on the solutions of the proposed model for cloud security, with the first method of traditional programming and the use of reactive programming as the most suitable solution in our case. While taking the measurements in this paper, we used the same algorithms, and we present comparative results between the first and second methods of programming. The results in the paper are presented in tables and graphs, which show that reactive programming in the proposed model for cloud security offers better results compared to traditional programming.