Reviewerly/exHarmony huggingface dataset:
5 Proficiency of Metaverse using virtual reality for industry and users perspective

Amid the various technologies being used such as AI, Cloud, IoT etc. widespread, the world of the web has seen a gigantic climb within the advancing and digitizing world. Digitalization of every resource and object has fundamentally become a part of our lives. It has helped to make use of resources in an efficient manner, which provides opportunities in various activities. Metaverse is one such technology that has introduced the digital world to our everyday lives. This technology has involuntarily provided an immersive platform to connect people with technology. The paradigm of the digital world is just beginning to take shape and reality as a Metaverse. It is still emerging, and many key components have started to take shape; it is now a major part of almost every field. MNCs such as Meta (formerly Facebook Inc.), Google, and Electronic Arts have invested huge amounts to set up dedicated infrastructure to improve the existing technology and make it ubiquitous. Several Metaverse worlds have launched their own digital currencies running on Blockchain technology to provide highly secure and seamless trading of digital real estates, avatars, and many more objects. In addition to the subject, it is seen that Immersive Reality (Virtual Reality and Augmented Reality) technologies, such as in the education sector and digital-marketing fields, show interest in this field of Metaverse. When combined with Artificial Intelligence, Metaverse provides a real feel to simulations of aircraft, medical surgeries, and war scenarios to train armed forces. This chapter presents an effort to showcase the ecosystem and the elements that Metaverse consists of. It examines the capabilities and application of the existing virtual worlds. Every tech giant and field has begun to implement Virtual Reality, coming up with positive innovative results. Therefore, we discuss the economical and industrial impact, as also the negative aspects of implementing Metaverse.
Dynamics of a multipartite hybrid quantum system with beamsplitter, dipole–dipole, and Ising interactions

The possibility of exploiting heterogeneous quantum systems to high precision, for storing, processing, and transmitting information, makes them ideal candidates for multitasking purposes in quantum communication. Appropriate quantum systems involving a judicious choice of interactions that augment each other are potentially useful for probing deep into quantum regimes. Here, we use one such hybrid bipartite quantum model, with one subsystem made of a pair of qubits and another comprising a pair of oscillators, to study the entanglement dynamics and the entanglement transfer between discrete and continuous variables. The basic model is the standard double Jaynes–Cummings model, which, under suitable conditions, is known to support both entanglement transfer and entanglement sudden death. In this work, we generalize this model to include further experimentally relevant interactions, such as the beamsplitter-type exchange interaction between the oscillators, and dipole–dipole and Ising-type interactions between the qubits. The way various interactions and initial oscillator states affect the entanglement dynamics is examined theoretically for generic experimental conditions. Using exact analytical solutions, we show that, compared to the beamsplitter or dipole–dipole interaction, the Ising interaction can have a significant positive impact on entanglement sudden death and birth, and the postponement of the onset of these phenomena, apart from producing a substantial reduction in the time duration of the death.
Leveraging Deep Learning Models for Automated Aspect Based Sentiment Analysis and Classification

Aspect-based Sentiment Analysis (ABSA) is a subdomain of Sentiment Analysis (SA) that focuses on detecting the sentiment toward features of a product or particular aspects, experience, or service.ABSA targets to go beyond simple sentiment classification of a sentence or document and present a more granular study of sentiment towards different aspects.ABSA has several real-time applications, which include social media monitoring, customer feedback analysis, and product reviews.Many difficulties exist in ABSA, including dealing with language variability and complexity, sentiment subjectivity, and managing multiple aspects in a single sentence.Recently, Deep Learning (DL) methods continued to be an active area of research and proved a promising model in ABSA.This study focuses on designing and developing ABSA models using DL concepts.The presented ABSA model aims to identify the sentiments in the direction of particular aspects or features of a product, service, or experience.The presented approach initially accomplishes diverse phases of data pre-processing to convert the input data meaningfully.In addition, the word2vec model is applied as a feature extraction approach.For sentiment analysis, three DL models are employed, namely Hopfield Network (HN), Convolutional Neural Network (CNN), and Bidirectional Long Short Term Memory (BiLSTM) approaches.The experimental validation of the DL models occurs utilizing a benchmark dataset.The simulation values highlighted that the CNN model exhibits improved sentiment classification results over other DL models.
Gaussian-Based Approach for Out-of-Distribution Detection in Deep Learning

When dealing with Deep Learning applications for open-set problems, detecting unknown samples is crucial for ensuring the model’s robustness. Numerous methods aim to distinguish between known and unknown samples by analyzing different patterns generated by the model. Among these methods, those that utilize the model’s output are considered the most widely applicable and practical for pre-trained models. Despite their effectiveness, there are also other techniques that can enhance Out-of-distribution detection methods by calibrating or transforming logit scores. In this study, we propose two approaches for out-of-distribution detection using logit transformation. One approach is based on the likelihood from a Gaussian distribution of logits. Additionally, we extend our method to a multivariate perspective using a mixture of Gaussian distributions to obtain better score disentanglement for traditional out-of-distribution detection methods. Our approaches were evaluated in various multi-class classification scenarios. The results showed that our logit transformation method using Gaussian distribution led to an improvement of up to 11% in terms of AUROC, and up to 32.6% in FPR95 if compared to other methods.
CT Images Segmentation Using a Deep Learning-Based Approach for Preoperative Projection of Human Organ Model Using Augmented Reality Technology

Over the last decades, facing the blooming growth of technological progress, interest in digital devices such as computed tomography (CT) as well as magnetic resource imaging which emerged in the 1970s has continued to grow. Such medical data can be invested in numerous visual recognition applications. In this context, these data may be segmented to generate a precise 3D representation of an organ that may be visualized and manipulated to aid surgeons during surgical interventions. Notably, the segmentation process is performed manually through the use of image processing software. Within this framework, multiple outstanding approaches were elaborated. However, the latter proved to be inefficient and required human intervention to opt for the segmentation area appropriately. Over the last few years, automatic methods which are based on deep learning approaches have outperformed the state-of-the-art segmentation approaches due to the use of the relying on Convolutional Neural Networks. In this paper, a segmentation of preoperative patients CT scans based on deep learning architecture was carried out to determine the target organ’s shape. As a result, the segmented 2D CT images are used to generate the patient-specific biomechanical 3D model. To assess the efficiency and reliability of the proposed approach, the 3DIRCADb dataset was invested. The segmentation results were obtained through the implementation of a U-net architecture with good accuracy.
Defect prediction of big data computer network based on deep learning model

Abstract Computer network software prediction is a good way to improve software quality, and the predictions of the software prediction method are close to the characteristics of the data set. In order to solve the problem that the invisible size of special data set is too large for computer software prediction, the author proposed a computer network-based software prediction method with deep computer coding and power learning. deep exploration of data features. data features. This type of model first uses an unsupervised learning-based evaluation model to evaluate the data set of 6 open projects, which solves the problem of classification uncertainty in the data; Deep self-encoding network models were then investigated. The model reduces the size of the data set, which is used to connect our model at the end of the model, the model uses training sets of shortened length to train the workers, and finally it makes predictions using the benchmarks. Experiments show that this model's prediction is better than standard software defect prediction, better than existing model-based software prediction models for fabric processes with repeated data volumes, and can be used in different categories. algorithms.
A Novel Steganography Method for Infrared Image Based on Smooth Wavelet Transform and Convolutional Neural Network

Infrared images have been widely used in many research areas, such as target detection and scene monitoring. Therefore, the copyright protection of infrared images is very important. In order to accomplish the goal of image-copyright protection, a large number of image-steganography algorithms have been studied in the last two decades. Most of the existing image-steganography algorithms hide information based on the prediction error of pixels. Consequently, reducing the prediction error of pixels is very important for steganography algorithms. In this paper, we propose a novel framework SSCNNP: a Convolutional Neural-Network Predictor (CNNP) based on Smooth-Wavelet Transform (SWT) and Squeeze-Excitation (SE) attention for infrared image prediction, which combines Convolutional Neural Network (CNN) with SWT. Firstly, the Super-Resolution Convolutional Neural Network (SRCNN) and SWT are used for preprocessing half of the input infrared image. Then, CNNP is applied to predict the other half of the infrared image. To improve the prediction accuracy of CNNP, an attention mechanism is added to the proposed model. The experimental results demonstrate that the proposed algorithm reduces the prediction error of the pixels due to full utilization of the features around the pixel in both the spatial and the frequency domain. Moreover, the proposed model does not require either expensive equipment or a large amount of storage space during the training process. Experimental results show that the proposed algorithm had good performances in terms of imperceptibility and watermarking capacity compared with advanced steganography algorithms. The proposed algorithm improved the PSNR by 0.17 on average with the same watermark capacity.
11 Role of augmented reality and virtual reality in sports

Computer-generated reality and expanded reality are part of comparative studies of HMDs. The two advances use high-goal shows and sensors to show stereoscopic pictures in head-mounted devices. In virtual reality (VR), the client sees the virtual environment. It shuts out impacts from the real world to amplify the experience. The system uses cloud functionalities. It overlays virtual images on real-world environment impeccably. The user cannot differentiate between genuine and virtual images while using an expanded reality headset. In augmented reality (AR) applications, virtual substances can significantly alter the present reality seen by the users by adding, eliminating, or changing a few parts of this reality. These applications need item location and acknowledgment in the preprocessing stage. The principal component of vision innovation has made headway in the last twenty years. Applications use a mix of AR and Principal Component (PC) vision. It includes game diversion applications. Enlisting AR and PC vision innovation into sports diversion applications opens new doors and comes with new challenges. This work is a survey article discussing the various contributions.
Understanding Insomnia from the Liver Based on Imagery Thinking

Insomnia is a common disease in a modern society with an accelerated pace of life, and its incidence covers the population Wide, has become a wide and profound social problem; at present, western drug treatment toxic side effects, therefore, small side effects and huge potential for insomnia treatment more and more attention and favor.The liver is just dirty, the main blood, Jue Yin liver is a length of diseases, liver internal viscera, the treatment of insomnia need to focus from the liver.Imagery thinking is the cornerstone of the development of traditional Chinese medicine, guiding the clinical medicine and acupoint selection of ancient doctors.Based on the traditional imagery thinking of traditional Chinese medicine, this paper summarizes and deeply explores the relationship between liver, liver meridian and insomnia, which provides a theoretical basis for preventing and controlling insomnia from the perspective of liver.Insomnia often appears in ancient Chinese medical texts with words such as no sleep and eyes not dark.The Spiritual Pivot -Dazed Theory says: "Wei Qi is not allowed to enter Yin, but always stays in Yang.The descriptions of insomnia in the Treatise on Typhoid include not being able to sleep and not being able to sleep.Chinese medicine believes that the liver collects blood, which is wood, to nourish the heart and mind, and if the heart and mind are not nourished, sleep is restless.According to the table of midnight-noon and ebb-flow doctrine, the liver meridian is at its peak at the ugly hour, from 1:00 AM to 3:00 AM, which is the time when people are most likely to have sleep disorders, indicating that ancient medical practitioners recognized that the cause of insomnia is closely related to the liver，to see table 1.
FI-FPN: Feature-integration feature pyramid network for object detection

The multi-layer feature pyramid structure, represented by FPN, is widely used in object detection. However, due to the aliasing effect brought by up-sampling, the current feature pyramid structure still has defects, such as loss of high-level feature information and weakening of low-level small object features. In this paper, we propose FI-FPN to solve these problems, which is mainly composed of a multi-receptive field fusion (MRF) module, contextual information filtering (CIF) module, and efficient semantic information fusion (ESF) module. Particularly, MRF stacks dilated convolutional layers and max-pooling layers to obtain receptive fields of different scales, reducing the information loss of high-level features; CIF introduces a channel attention mechanism, and the channel attention weights are reassigned; ESF introduces channel concatenation instead of element-wise operation for bottom-up feature fusion and alleviating aliasing effects, facilitating efficient information flow. Experiments show that under the ResNet50 backbone, our method improves the performance of Faster RCNN and RetinaNet by 3.5 and 4.6 mAP, respectively. Our method has competitive performance compared to other advanced methods.
PCA-Enhanced Autoencoders for Nonlinear Dimensionality Reduction in Low Data Regimes

Many scientific domains, such as nanophotonic design, gene expression, and materials design, are limited by high costs of acquiring data.This data is often intrinsically low-dimensional, nonlinear, and benefits from dimensionality reduction.Autoencoders (AE) provide nonlinear dimensionality reduction but are typically ineffective for low data regimes.Principal Component Analysis (PCA) is data-efficient but limited to linear dimensionality reduction.We propose a technique that harnesses the benefits of both methods by using PCA to initialize an AE.The proposed approach outperforms both PCA and standard AEs in low-data regimes and is comparable to the best of either of the two in other scenarios.
A Comparative Study on Readability of High School English Textbooks in Poland and China

In the process of China rapidly entering the center of the world stage, participating in global governance, and jointly building the "the Belt and Road", it is urgent to cultivate talents who are proficient in foreign languages.English textbooks play a key role in the education and training of English talents.This paper analyzes the text readability of high school English textbooks in China and in Poland, one of the important countries along the "the Belt and Road".The similarities and differences between the two kinds of high school English textbooks have been found which can be used for further revision of high school English textbooks in the future.
Automatic Class Extraction from Spanish Text of User Stories Using Natural Language Processing

During the software development process, one of the most important stages is the requirements analysis, since in this stage the requirements supplied by the client or end user are analyzed and the system structure is defined, which is essential for the design stage. However, it is a process in which multiple inconsistencies can occur due to misunderstanding of the requirements or excessive time in understanding them. This paper presents an approach in the use of Natural Language Processing for the automatic extraction of classes of a software from the user stories in Spanish language for its design and the comparison of the results obtained against the classes obtained manually. Finally, it is proposed for a future work, the extraction of more characteristics of the classes with the objective of improving their classification, as well as the construction of a dataset focused on the Spanish language to reduce the possible noise in the results.
A Three-Dimensional Shape Measurement for Large Deep-Cavity Parts

In manufacturing, it is important to measure large, complex, deep-cavity parts with high accuracy. Due to the large size of the part itself and the complex structure of its inner and outer surface, the traditional three-dimensional (3D) optical measuring system is more difficult to measure. In this paper, a collaborative measurement system for large-size complex deep-cavity parts is proposed based on high-dynamic range N-step fringe projection profilometry and parallel single-pixel imaging technology. By collaborative measurements, the 3D point cloud of internal and external surfaces of parts are reconstructed, and the measurement results are stitched. Experimental results show that the system can accurately measure the internal and external surface of complex deep-cavity parts and stitch the point cloud data with high accuracy.
Style-constrained Takagi-Sugeno-Kang Fuzzy Classifier

Due to the excellent interpretability and classification performance, the Takagi-Sugeno-Kang fuzzy classifier (TSK-FC) has drawn great attention. However, different patterns own their respective homogeneities, and the samples from different groups present explicitly or implicitly homogenous styles, which are significantly different from the assumption that all samples from different groups are identically and independently distributed (i.i.d.). In this paper, a style-constrained Takagi-Sugeno-Kang fuzzy classifier called SC-TSK-FC is proposed by breaking the i.i.d. assumption. To explore the styles of data, a series of style matrices are embedded into the objective function of the TSK-FC. Besides, with the introduction of the regularization term corresponding to each style matrix, the nuances between different styles of data can be captured, which can help improve the classification performance of SC-TSK-FC. Particularly, five fixed fuzzy partitions with interpretable linguistic terms are adopted to along each input feature, and SC-TSK-FC outperforms TSKFC by using less fuzzy rules. These guarantee the interpretability of SC-TSK-FC. Experimental results on some benchmark datasets demonstrate the effectiveness of the proposed SC-TSK-FC.
Design of Artificial Intelligence Teaching System in Colleges and Universities Based on Collaborative Filtering Algorithm

One of the important goals of online learning is to realize personalized learning service based on intelligent technology, so as to promote personalized education on a large scale. Therefore, the intelligent and personalized transformation of teaching resource service has become two core tasks. Taking this as the goal, this paper aims to realize personalized learning service for learners by reorganizing teaching resources based on knowledge map technology and supporting the evaluation and analysis of learners' learning level and needs. Therefore, the related technology research and system prototype realization are carried out, and a personalized learning platform based on knowledge map is formed. It combines the learning of knowledge points, the training of exercises and personalized recommendation to form a closed loop of personalized learning services. In the daily teaching activities of colleges and universities, the course selection system can not only make students clearly understand the key points of a certain class; At the same time, it can also enable students to make their own choices better and define their own development direction. Therefore, the course selection system should be able to push and recommend courses that students are really interested in, so that every student can choose and take courses that they like and really need.
Research on Multi-robot Pursuit Control Based on Distributed Intelligent Algorithm

The coordinated control of multi-robot systems is a hot research topic in robot control in recent years. This paper mainly focuses on the coordinated control of multi-robot pursuit and evasion task, and analyzes the motion strategy of both sides in the obstacle environment. Firstly, a task allocation model based on distribution is established. Secondly, the pilot-following method is used to determine the formation pursuit strategy of the pursuit robots, and the moving target points of the pursuit robots are solved in the barrier-free environment based on differential games. Finally, the obstacle avoidance angle is analyzed and calculated according to the sensor information, and the real-time motion strategy of each robot is obtained by combining the fuzzy logic algorithm. The simulation results show that the distributed intelligent fusion algorithm can complete the pursuit task in real time efficiently, and the pursuit robot can maintain a certain formation during the pursuit process. The research results are not only applicable to the problem of multi-robot pursuit, but also have certain reference significance for the analysis of pursuit tasks in UAV and military field.
PENGARUH MEDIA AUDIO-VISUAL DALAM PEMBELAJARAN BAHASA ARAB TERHADAP SISWA KELAS V DI MIS AL-HIDAYAH CK

In an era that has increasingly sophisticated technology, a teacher must make the best use of it. So, the learning carried out in schools can be varied and interesting and fun to follow the learning process and not monotonous like that. That’s all from year to year, using audio media. Visual is one methode that can be used for learning process. So, students can enjoy learning during the learning process and can understand learning easily.