Reviewerly/exHarmony huggingface dataset:
Towards Quantum Gravity in the Lab on Quantum Processors

The holographic principle and its realization in the AdS/CFT correspondence led to unexpected connections between general relativity and quantum information. This set the stage for studying aspects of quantum gravity models, which are otherwise difficult to access, in table-top quantum-computational experiments. Recent works have designed a special teleportation protocol that realizes a surprising communication phenomenon most naturally explained by the physics of a traversable wormhole. In this work, we have carried out quantum experiments based on this protocol on state-of-the-art quantum computers. The target quantum processing units (QPUs) included the Quantinuum's trapped-ion System Model H1-1 and five IBM superconducting QPUs of various architectures, with public and premium user access. We report the observed teleportation signals from these QPUs with the best one reaching 80% of theoretical predictions. We outline the experimental challenges we have faced in the course of implementation, as well as the new theoretical insights into quantum dynamics the work has led to. We also developed QGLab – an open-source end-to-end software solution that facilitates conducting the wormhole-inspired teleportation experiments on state-of-the-art and emergent generations of QPUs supported by the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Q</mml:mi><mml:mi>i</mml:mi><mml:mi>s</mml:mi><mml:mi>k</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>t</mml:mi><mml:mi>k</mml:mi><mml:mi>e</mml:mi><mml:mi>t</mml:mi></mml:math> SDKs. We consider our study and deliverables as an early practical step towards the realization of more complex experiments for the indirect probing of quantum gravity in the lab.
Bioengineering and Geomatics: Automatic Brain Image Segmentation using Two-Stage Pipeline with SNN and Watershed Algorithm

Digital image processing holds an increasingly essential role in the medical domain. This study emphasizes the significance of researching and implementing methods aimed at the segmentation of critical image regions and potential noise reduction, which is indispensable for medical professionals in disease diagnosis. Consequently, the investigation of software solutions in this context can substantially enhance diagnostic accuracy. In particular, neurology stands as a medical field wherein imaging plays a substantial contributory role. In pursuit of an automated brain image segmentation approach, this paper centers its attention on a two-step pipeline methodology to address the segmentation challenges inherent in medical imaging. The proposed method incorporates the use of a Self-Normalizing Neural Network (SNN) for denoising and employs the Watershed algorithm, typically employed in Geomatics imagery, for segmentation. Encouraging results are obtained, with a segmentation performance, as measured by IoU, reaching a noteworthy value of 0.93 when compared with alternative segmentation software.
Research on the innovative integration application of metaverse and education

The application prospect of education in the metaverse during the COVID-19 epidemic is a vital research topic. This paper focuses on analyzing the dilemmas in the development of contemporary education and the underlying technical features of the metaverse. By analyzing the feasibility of integrating the metaverse with education, this paper tries to explore the application prospect of innovative integration of metaverse and education. Therefore, this paper concludes that the education in metaverse can not only make up for the shortcomings of face-to-face teaching, but also break through the current development bottleneck of online education, balance educational resources, promote education equity, and thus maintain fairness and justice of the whole society.
Computing Shintani domains

For a number field [Formula: see text] having at least one embedding into the real numbers we give an algorithm to obtain a Shintani domain for the action of the totally positive units of [Formula: see text] under its geometric embedding. Our algorithm modifies a known signed, or virtual, fundamental domain until it becomes a true one. We examine the results of extensive runs of our algorithm for cubic, quartic and quintic number fields. We also give some data for totally real sextic fields where our algorithm is still often practical.
PEMBENTUKAN KARAKTER GEMAR MEMBACA PADA ANAK USIA DINI MELALUI MEDIA BUKU CERITA BERGAMBAR

This article aims to explain the importance of maintaining character education, especially for early childhood to cultivate a sense of reading through the media of picture storybooks. This article uses the Literature study method by examining library data such as books, magazines, documents, historical stories, or related research objects. The formation of characters like to read children is very important because to prepare children to face the elementary school level. Early childhood growth and development is very rapid. Then the ability to read in children needs to be considered. By reading children will gain new information, knowledge and experiences. Remembering the childhood is a true adventure and learning that is full of honesty in realizing his thoughts and expressing his feelings. All parents certainly want to make their children happy, seeing them grow up healthy, intelligent, and successful in their lives actively in moving so that children are healthy both physically and spiritually.
Augmented Reality Based Indoor Navigation System

Abstract: Every people, at some point in their life, may get lost inside a large auditorium, struggled to find their exit point at the airport, or may get late to a lecture because they could not find the right lecture hall at the university. This paper addresses the development of an Augmented Reality Based Indoor Navigation Application. It is based on four main modules, 3D model building, shortest path calculation, QR code scanning and path visualization. The application is developed in Unity using AR Core XR plugin management and AR Foundation plugin. ZXing library is used for scanning and decoding the QR codes. We aim at developing the front end in the simplest way possible so that the users can easily reach their destination by just opening the camera where the directions are shown as animations in their surroundings.
LAA: Learn the Arabic Alphabet: Integrating Gamification Elements with Touchscreen Based Application to Enhance the Understanding of the Arabic Letters Forms

Touchscreen devices provide a variety of features and engaging interaction which offer a significant incentive for them to be adopted for learning purposes. They facilitate access to educational resources at any time and broaden the study contents beyond the formal curricula of educational institutions. This is promoted by employing educational applications that stimulate learners to interact effectively with the learning material to gain motivation, influencing their accomplishments in the targeted subject. However, some applications fail to provide a variety of feedback to their stakeholders by relying on evaluating learners either negatively or positively in a simplistic manner and with a lack of reinforcement. This unsuitability and the lack of feedback can lead to a weak level of interaction and decrease students' comprehension levels. In this paper, a multi-platform touchscreen-based application has been developed by adopting gamification to support learners with limited literacy skills in acquiring the Arabic alphabet. A study was conducted on 75 native Arabic learners studying in the first and second year of elementary level in Saudi Arabia to evaluate the system's ability to increase learners’ knowledge of the Arabic alphabet. The sample was divided into three groups; experiment, and control groups (both first-year) and second-year groups. The experimental group utilized the application after taking the pre-test, while the control group interacted with the learning material traditionally with their teachers. The second-year group results assisted the study in investigating the system's ability to rapidly boost the experiment group's comprehension of the targeted subject since the second-year learners were more experienced in Arabic letters than the experiment or control groups. The results of the comparison of the pre-test and post-test showed that the experiment group overtook the control and the second-year group regarding the post-test score, which indicates the system's ability to increase learners’ knowledge level. By providing an interactive and engaging learning experience, the developed application has shown the ability to increase learners' knowledge levels significantly. These research findings have implications for educators, learners with limited literacy skills, curriculum developers, and researchers who are seeking effective tools to improve language learning outcomes.
Variational quantum circuit learning of entanglement purification in multiple degrees of freedom

Entanglement purification is a crucial technique for promising the effective entanglement channel in noisy large-scale quantum networks, yet complicated in designing protocols in multi-degree of freedom (DoF). To execute the above tasks easily and effectively, developing a learning framework for designing the entanglement purification with multi-DoF is a promising way and still an open research question. Inspired by variational quantum circuit (VQC) with remarkable advantage in learning optimal quantum operations with near-term quantum devices, in this paper we propose an effective VQC framework for the entanglement purification in multi-DoF and exploit it to learn the optimal purification protocols of the objective function which are based on postselection. By properly introducing additional circuit lines for representing each of the ancillary DoFs of all the particles, e.g., space and time, the parametrized quantum circuit can effectively simulate scalable entanglement purification. To verify our framework, the well-known protocols in linear optics are learned well with alternative operations in low-depth quantum circuit. Moreover, we simulate the multipair cases with multi-DoF to show the scalability and discover one-round protocols. Our work provides an effective way for exploring the entanglement purification protocols in multi-DoF and multipair with near-term quantum devices.
Best Neural Network Approximation by using Bernstein Polynomials with GRNN Learning Application

Bernstein polynomials are one of the first and main tools for function approximation. On the other hand, neural networks have many useful applications in approximation and other fields as well. In this paper, we study how we benefit from properties of Bernstein polynomials to define a new version of neural networks, that can be fit approximating functions in terms of modulus of continuity. Numerically, we use neural networks to approximate some types of continuous functions. For that purpose, we use GRNN algorithm to approximate functions uniformly by using Matlab, giving some examples that confirm good rate approximation.
Entanglement Correlation from a Non-degenerate Three-Level Laser with a Parametric Amplifier and Coupled to Vacuum Reservoir

The quantum properties of a non-degenerate three-level laser with the parametric amplifier and coupled to a thermal reservoir are thoroughly analyzed with the use of the pertinent master equation and stochastic differential equations associated with the normal ordering. Applying solutions of resulting differential equations, quadrature variance, the mean and variance of photon number, the photon number correlation are calculated. However, the two-mode driving light has no effect on the squeezing properties of the cavity modes. Employing the same solutions, one can also obtain anti normally ordered characteristic function defined in the Heisenberg picture. For a linear gain coefficient of (A = 100), for a cavity damping constant of K= 0:8, μ = 0.16 and for thermal reservoir nth = 0, the maximum intra cavity photon entanglement is found at steady state and at threshold to be 70%.
LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation Function

In electronic warfare systems, detecting low-probability-of-intercept (LPI) radar signals poses a significant challenge due to the signal power being lower than the noise power. Techniques using statistical or deep learning models have been proposed for detecting low-power signals. However, as these methods overlook the inherent characteristics of radar signals, they possess limitations in radar signal detection performance. We introduce a deep learning-based detection model that capitalizes on the periodicity characteristic of radar signals. The periodic autocorrelation function (PACF) is an effective time-series data analysis method to capture the pulse repetition characteristic in the intercepted signal. Our detection model extracts radar signal features from PACF and then detects the signal using a neural network employing long short-term memory to effectively process time-series features. The simulation results show that our detection model outperforms existing deep learning-based models that use conventional autocorrelation function or spectrogram as an input. Furthermore, the robust feature extraction technique allows our proposed model to achieve high performance even with a shallow neural network architecture and provides a lighter model than existing models.
Adversarially Learning Occlusions by Backpropagation for Face Recognition

With the accomplishment of deep neural networks, face recognition methods have achieved great success in research and are now being applied at a human level. However, existing face recognition models fail to achieve state-of-the-art performance in recognizing occluded face images, which are common scenarios captured in the real world. One of the potential reasons for this is the lack of large-scale training datasets, requiring labour-intensive and costly labelling of the occlusions. To resolve these issues, we propose an Adversarially Learning Occlusions by Backpropagation (ALOB) model, a simple yet powerful double-network framework used to mitigate manual labelling by contrastively learning the corrupted features against personal identity labels, thereby maximizing the loss. To investigate the performance of the proposed method, we compared our model to the existing state-of-the-art methods, which function under the supervision of occlusion learning, in various experiments. Extensive experimentation on LFW, AR, MFR2, and other synthetic masked or occluded datasets confirmed the effectiveness of the proposed model in occluded face recognition by sustaining better results in terms of masked face recognition and general face recognition. For the AR datasets, the ALOB model outperformed other advanced methods by obtaining a 100% recognition rate for images with sunglasses (protocols 1 and 2). We also achieved the highest accuracies of 94.87%, 92.05%, 78.93%, and 71.57% TAR@FAR = 1 × 10−3 in LFW-OCC-2.0 and LFW-OCC-3.0, respectively. Furthermore, the proposed method generalizes well in terms of FR and MFR, yielding superior results in three datasets, LFW, LFW-Masked, and MFR2, and producing accuracies of 98.77%, 97.62%, and 93.76%, respectively.
Multisensory System for Long-Term Activity Monitoring to Facilitate Aging-in-Place

Demographic changes and an ageing population require more effective methods to confront the increased prevalence of chronic diseases which generate dependence in older adults as well as an important rise in social expenditure. The challenge is not only to increase life expectancy, but also to ensure that the older adults can fully enjoy that moment in their lives, living where they wish to (private home, nursing home, …). Physical activity (PA) is a representative parameter of a person's state of health, especially when we are getting older, because it plays an important role in the prevention of diseases, and that is the reason why it is promoted in older adults. One of the goals of this work is to assess the feasibility of objectively measuring the PA levels of older adults wherever they live. In addition, this work proposes long-term monitoring that helps to gather daily activity patterns. We fuse inertial measurements with other technologies (WiFi- and ultrasonic-based location) in order to provide not only PA, but also information about the place where the activities are carried out, including both room-level location and precise positioning (depending on the technology used). With this information, we would be able to generate information about the person's daily routines which can be very useful for the early detection of physical or cognitive impairment.
Evolutionary Equilibrium of Mining Pools Under DDoS Attack in Blockchain System

Consensus algorithm is widely used in blockchain system, which can solve the problems of fault tolerance, consensus and security. Proof-of-work (PoW) algorithm is one of the most widely used consensus algorithms to ensure the security and consensus on transaction data of the Bitcoin system. However, as an important part of the Bitcoin system, mining pool is often subjected to distributed denial of service (DDoS) attack by malicious nodes at the network layer, which makes it suffer significant economic losses. In order to reduce the loss of mining pools caused by DDoS attack when the Bitcoin system carries out data consensus of PoW, this paper proposes an evolutionary game model based on mining pools. This model studies how to choose effective mining strategy to get the best profit in the face of DDoS attack. At the same time, we also set up the replication dynamic equation to analyze and solve the game model. The experimental results show that honest mining has the best profit under the condition of poor network for the mining pools. In the case of better network condition, the mining pools can get the best reward by attacking each other. At the same time, the experimental results are consistent with the theoretical conclusions, which proves that our model is effective.
Adaptation of YOLOv7 and YOLOv7_tiny for Soccer-Ball Multi-Detection with DeepSORT for Tracking by Semi-Supervised System

Object recognition and tracking have long been a challenge, drawing considerable attention from analysts and researchers, particularly in the realm of sports, where it plays a pivotal role in refining trajectory analysis. This study introduces a different approach, advancing the detection and tracking of soccer balls through the implementation of a semi-supervised network. Leveraging the YOLOv7 convolutional neural network, and incorporating the focal loss function, the proposed framework achieves a remarkable 95% accuracy in ball detection. This strategy outperforms previous methodologies researched in the bibliography. The integration of focal loss brings a distinctive edge to the model, improving the detection challenge for soccer balls on different fields. This pivotal modification, in tandem with the utilization of the YOLOv7 architecture, results in a marked improvement in accuracy. Following the attainment of this result, the implementation of DeepSORT enriches the study by enabling precise trajectory tracking. In the comparative analysis between versions, the efficacy of this approach is underscored, demonstrating its superiority over conventional methods with default loss function. In the Materials and Methods section, a meticulously curated dataset of soccer balls is assembled. Combining images sourced from freely available digital media with additional images from training sessions and amateur matches taken by ourselves, the dataset contains a total of 6331 images. This diverse dataset enables comprehensive testing, providing a solid foundation for evaluating the model’s performance under varying conditions, which is divided by 5731 images for supervised system and the last 600 images for semi-supervised. The results are striking, with an accuracy increase to 95% with the focal loss function. The visual representations of real-world scenarios underscore the model’s proficiency in both detection and classification tasks, further affirming its effectiveness, the impact, and the innovative approach. In the discussion, the hardware specifications employed are also touched on, any encountered errors are highlighted, and promising avenues for future research are outlined.
Boolean Searchable Attribute-Based Signcryption With Search Results Self-Verifiability Mechanism for Data Storage and Retrieval in Clouds

Storing and sharing confidential data in a public cloud storage system always raises privacy and security issues. When data is outsourced to a cloud server, the basic demands are secure and authenticated data storage, fine-grained data access control, secure search for the outsourced data, and search results verification. Attribute-Based Signcryption (ABSC) is a promising public-key cryptosystem for accomplishing data confidentiality and authenticity together. However, incorporating verifiable Boolean formula based keyword search and verifiable outsourced unsigncryption mechanisms into an ABSC is quite challenging. This is because the data user should be able to check whether the search result, returned by the untrusted cloud, has been correctly created. Precisely, the data user should be able to verify the correctness of the search, transform and signature verification operations involved in generating the search result. In this paper, for the first time, we propose a secure Searchable Attribute-Based Signcryption (sABSC) scheme that simultaneously supports (i) Boolean formula search over signcrypted data, (ii) keyword privacy, (iii) verifiable outsourced unsigncryption, and (iv) search results self-verifiability. In sABSC, each data user can efficiently verify the correctness of the search results returned by the cloud without interacting with any authority. We define more general security definitions of sABSC and provide rigorous security analysis. Performance evaluation exhibits that the proposed sABSC is practical.
DeepSportradar-v2: A Multi-Sport Computer Vision Dataset for Sport Understandings

Advanced data collection technologies, computational tools, and sophisticated algorithms have a revolutionary impact on sports analytics on various aspects of sports, from athletes performance to fan engagement. Computer Vision (CV) and Deep Learning (DL) technologies play a crucial role in predicting players and game states from videos, but their effectiveness depends on the quantity and quality of training data, especially in sports with unique dynamics and camera angles. Each sport comes with its own set of challenges.
Heartful Education. Benefits from Teaching Unity in Schools

Several papers indicate the benefits of an educational model based on the teaching of values, character strengths, and both academic and non-academic contents. In this paper, we proceed to review the most significant and relevant articles regarding this holistic educational approach which considers the spiritual heart as a fundamental pillar to connect mind, body, and spirit. The concepts of heartful education, values and character strengths are examined, to stress the need of a new and innovative approach in education that considers teaching unity in schools as a method to provide a type of education that intends to cover the whole spectrum of learners’ needs. It is claimed in this article the benefits of providing such educational model and it is also suggested the possibility to create a subject named “unity” within the schools’ curriculum so that learners are consistently reminded of the countless aspects that they all have in common. This paper shows the conclusions and beneficial repercussions of this holistic and heartful educational approach.